HomeMy WebLinkAboutTransportation Packet April 2021
Note: Anyone wishing to speak at any Transportation Commission meeting is encouraged to do so. If you wish to speak, please rise and, after you
have been recognized by the Chair, give your name and complete address for the record. You will then be allowed to speak. Please note the public
testimony may be limited by the Chair.
AASSHHLLAANNDD TTRRAANNSSPPOORRTTAATTIIOONN CCOOMMMMIISSSSIIOONN
AApprriill 1155,, 22002211
AGENDA
I. CALL TO ORDER: 6:00 PM, Meeting held virtually via Zoom
II. ANNOUNCEMENTS
III. CONSENT AGENDA
A. Approval of Minutes: March 18, 2021
IV. PUBLIC FORUM (6:05-6:20)
A. Public Forum-if you wish to speak during public forum please register with Shannon.burruss@ashland.or.us by 10am April
14th.
B. If you wish to provide public comment or discuss an agenda item please contact Shannon.burruss@ashland.or.us by April
14th by 10am to register to participate. Written comments can also be submitted in the same time frame.
C. If you are interested in watching the meeting via Zoom please utilize the following link:
V. CRASH REPORT (6:20-6:30)
VI. NEW BUSINESS
A. None
VII. OLD BUSINESS
A. 20 Is Plenty Program (6:30-7:30, action required, discuss programmatic development of the 20 Is Plenty Program)
B. Evacuation Time Estimate Draft Report (7:30-8:00, no action required, review and discuss draft report)
VIII. TASK LIST (If time allows)
A. Discuss current action item list
IX. FOLLOW UP ITEMS
A. Bike Map Development
X. INFORMATIONAL ITEMS (If time allows)
A. Normal Avenue Traffic Calming
XI. COMMISSION OPEN DISCUSSION (If time allows)
XII. FUTURE AGENDA TOPICS
A. TSP Update
B. Residential Parking Program
C. Street User Fee/Gas Tax (budget process)
D. Crosswalk Policy
XIII. ADJOURNMENT: 8:00 PM
Next Meeting Date: May 20, 2021 Meeting
In compliance with the Americans with Disabilities Act, if you need special assistance to participate in this meeting, please contact the Public Works Office at 488-5587 (TTY phone number 1 800
735 2900). Notification 48 hours prior to the meeting will enable the City to make reasonable arrangements to ensure accessibility to the meeting (28 CFR 35.102-35.104 ADA Title I).
Transportation Commission
Contact List as of February 2021
Name Title Telephone Mailing Address Email Address Expiration
of Term
Mark Brouillard Commissioner 206-661-7085 159 Helman St mtbrouillard@msn.com 4/30/2023
Joe Graf Commissioner 541-488-8429 1160 Fern St. jlgtrans15@gmail.com 4/30/2021
Corinne Vièville Commissioner 541-488-9300 or
541-944-9600 805 Glendale Ave. corinne@mind.net 4/30/2022
Derrick Claypool-Barnes Commissioner 503-482-9271 1361 Quincy St #6F dorkforest@gmail.com 4/30/2021
Linda Peterson Adams Commissioner 541-554-1544 642 Oak St gardengriotashland@gmail.com 4/30/2022
Katharine Danner Commissioner 541-482-2302 PO Box 628 ksdashland@gmail.com 4/30/2022
Bruce Borgerson Commissioner 541-488-5542 209 Sleepy Hollow Dr wave@mind.net 4/30/2023
Non-Voting Ex Officio Membership
Scott Fleury Director, Public Works 541-488-5587 20 E. Main Street scott.fleury@ashland.or.us
Paula Hyatt Council Liaison 20 E. Main Street Paula.Hyatt@council.ashland.or.us
Brandon Goldman Planning Department 541- 488-5305 20 E. Main Street goldmanb@ashland.or.us
Steve MacLennan Police Department 541- 552-2433 20 E. Main Street maclenns@ashland.or.us
Vacant SOU Liaison 541-552-8328 1250 Siskiyou Blvd
Dan Dorrell, PE ODOT 541- 774-6354 100 Antelope Rd WC 97503 Dan.w.dorrell@odot.state.or.us
Edem Gómez RVTD 541-608-2411 3200 Crater Lake Av 97504 egomez@rvtd.org
Jenna Stanke ODOT 541- 774-5925 100 Antelope Rd WC 97503 Jenna.MARMON@odot.state.or.us
David Wolske Airport Commission david@davidwolske.com
Vacant Ashland Parks
Vacant Ashland Schools
Staff Support
Scott Fleury Public Works Director 541-488-5347 20 E. Main Street Scott.fleury@ashland.or.us
Karl Johnson Associate Engineer 541-552-2415 20 E. Main Street johnsonk@ashland.or.us
Shannon Burrus Permit Technician 541-552-2428 20 E. Main Street Shannon.burrus@ashland.or.us
ASHLAND TRANSPORTATION COMMISSION
MINUTES
March 18th, 2021
Transportation Commission
March 18th, 2021
Page 1 of 2
These minutes are pending approval by this Commission
CALL TO ORDER: 6:00pm
Commissioners Present: Mark Brouillard, Joe Graf, Corinne Vièville, Linda Peterson Adams, Katharine Danner,
Bruce Borgerson, Derrick Claypool-Barnes
Commissioners Not Present- None
Council Liaison Present: Paula Hyatt
Staff Present: Scott Fleury
Guests Present: Mojie Takallou
ANNOUNCEMENTS – None
CONSENT AGENDA
Approval of Minutes 02.18.21
Vièville motions to approve minutes, Borgerson seconds.
All ayes, motion carries.
PUBLIC FORUM -None
ACCIDENT REPORT: Officer MacLennan not present. Borgerson discusses the similarity between an accident he’d
had in 2009 to the first accident report listed. Commission discusses current downtown three lane configuration, in
relation to accidents.
NEW BUSINESS
A. Local Road and Street Safety for non-engineers presentation by Mojie Takallou- Takallou presents slide
show information contained presentation (see attached). Commission and Staff discuss traffic safety goals,
stating the primary goal is zero traffic fatalities and disabling injuries. Takallou leads conversation with
Commission by asking questions contained in presentation.
Old Business-
A. Capital Improvement Program- Peterson Adams introduces Fleury for his rundown of his report to City
Counsel. Links to full document and staff’s background report can be found in meeting agenda. Fleury
relays that he presented the recommendations from the Commission to Council and lays out next steps and
budget process timelines. Graff asks about the TSP update in relation to Downtown, Fleury recalls that
when the project was stopped it was anticipated to be readdressed after two years, so it is his opinion that
the Downtown Project is separate from the TSP, but he does state that smaller related projects could be
added to the TSP in order to create a backbone that the project can build from once it starts back up.
Commission intends to interject themselves as much as possible in the Downtown Revitalization Project in
order to stress the importance of the project in the Commission’s multi modal transportation goals.
Commission would like to revisit the conversation on future agenda topics to formalize the will of the
Commission.
TASK LIST
A. Discuss current action item list
FOLLOW UP ITEMS
A. Bike Map Development- Discussion only on what is occurring with the former Zagster bikes, Fleury relays
that a project is in the works, Claypool-Barnes offers to act as Transportation Commission liaison to the
ASHLAND TRANSPORTATION COMMISSION
MINUTES
March 18th, 2021
Transportation Commission
March 18th, 2021
Page 2 of 2
These minutes are pending approval by this Commission
project and requests contact information from Fleury.
INFORMATIONAL ITEMS-
A. Normal Avenue Traffic Calming Application
B. Tolman Creek Intersection Advance Plans (ODOT)
COMMISSION OPEN DISCUSSION-
FUTURE AGENDA TOPICS
A. TSP Update
B. 20 Is Plenty Program
C. Residential Parking Program
D. Street User Fee/Gas Tax (budget process)
E. Crosswalk Policy
ADJOURNMENT:
Respectfully submitted,
Shannon Burruss Permit Technician-Engineering and Public Works
**Full Video Available by Request**
Prepared by
Mojie Takallou, Ph.D, P.E.
Department of Civil Engineering
University of Portland
Prepared by
Mojie Takallou, Ph.D, P.E.
Department of Civil Engineering
University of Portland
Sponsored by
U.S. Department of Transportation
National Highway Traffic Safety
Administration &
Transportation Safety Division
of the Oregon Department of
Transportation
Sponsored by
U.S. Department of Transportation
National Highway Traffic Safety
Administration &
Transportation Safety Division
of the Oregon Department of
Transportation
ROAD AND STREET SAFETY FOR NON-ENGINEERS
National Highway Traffic Safety Administration:
Occupants and Nonoccupants Killed in Traffic Crashes,
2018-2019
2018 Oregon Motor Vehicle Traffic Crashes Quick
Facts (based on data available as of 07/08/2020)
2018 Oregon Motor Vehicle Traffic Crashes Quick
Facts (based on data available as of 07/08/2020)
2018 Oregon Motor Vehicle Traffic Crashes Quick
Facts (based on data available as of 07/08/2020)
Jurisdictional Data for Jackson
County, 2018
Jackson County
Population Est. 219,200
Fatalities 32
Alcohol Involved Fatalities 10
Fatal & Injury Crashes 1,575
F&I Crashes / 1,000 Population 7.19
Nighttime F&I Crashes 238
Jurisdictional Data for Jackson
County Cities, 2018 (>10k)
Ashland Central Point Medford
Population Est. 20,815 17,895 80,375
Fatalities --5
Alcohol Involved Fatalities --3
Fatal & Injury Crashes 79 55 810
F&I Crashes
/ 1,000 Population
3.80 3.07 10.08
Nighttime F&I Crashes 11 5 81
Objectives
Discuss the problems, challenges of Roads and
Streets safety for the 21st century
Identify the types, causes, and costs of traffic
crashes
Review the importance of the 4 E’s of Roads and
Streets safety
Identify best practical and low-cost improvements
for making roads safer
To get everyone thinking about highway safety and
its place in their lives
CHALLENGES
Limited…
•Budgets
•Staffing
•Time
•Crash data
•Traffic and road
information
•Understanding or
awareness of safety
issues
•Training
In addition to…
•Lack of coordination
between agencies
•Competing priorities
•Politics
•Staff turnover
•Empowerment
CRASH or ACCIDENT?CRASH or ACCIDENT?
Use the term Crash in place of accident
ACCIDENT implies “no fault” when
CRASHES are actually caused because
someone used poor driving strategy &
tactics
Road
Environment
Factors (28%)
Vehicle
Factors (8%)
Human
Factors (95%)
4$4%
%
24%67%4%
4%
TYPICAL REPORTED CRASH CAUSES
32.
25.7
4.4
26.2
14.4
11.6
6.6
4.7
Motor Vehicle Crashes in the US,
2018
Population 327,167,434
Persons Killed 36,560
Persons Injured 2,710,000
Property Damage Crashes 4,807,000
Total Motor Vehicle Crashes 6,734,000
Economic Cost of Motor Vehicle
Crashes
$ 445.5 billion
Source: USDOT, NHTSA,NSC
Lives Saved (2017)
Seat Belts 14,955
Frontal Airbags 2,790
Motorcycle Helmets 1,872
Minimum Drinking age laws 538
Child Restraints 325
Motor Vehicle Crashes Quick
Facts USA (2018)
Pedestrian Fatalities 6,283
Motorcyclist Fatalities 4,985
Pedalcyclist Fatalities 857
Alcohol-Impaired Fatalities 10,511
Speed related Fatalities 9,378
Traffic Crashes From
September 11th, 2001 to September 2020
Fatalities approx.
707,000
Injuries approx.
48,000,000
Number of Crashes
approx. 113,500,000
Cost approx. $5.1
Trillion
On 9-11, approximately 3,030 innocent people lost their lives.
September 11 2001 to September
2020
707,000 Fatalities
Portland to Sacramento
= 580 miles
Average Height
= 5’ 3”
September 11, 2001 to September
2020
48,000,000
Injuries
Average Height
= 5’ 3”
Earth
Circumference
24,901 miles
238,900 Miles
Cost of Traffic
Crashes
Total Cost
=
$5,100,000,000,000
September 11, 2001 to September
2020
Oregon Traffic Crashes, 2018
502 Fatalities
79 Pedestrians Fatalities
9 Pedalcyclist Fatalities
85 Motorcyclist Fatalities
Source: ODOT
Oregon Summary of Traffic
Fatalities, 2018
Source: Oregon Department of Transportation
Number Percent
Total Fatalities 502
Speed 146 29.00%
Alcohol +Drug 142 28.30 %
P.S. some fatalities have multiple contributing circumstances
Distracted Driving
Drowsy and Distracted Drivers
Distracted by something inside or outside the vehicle
Texting
Using Cell phone
Eating and drinking
Talking to passengers
Using a navigation system
Adjusting a radio, CD player
Drowsy or fatigued
Concentrating on something other than driving
What Are Your Agency’s
Highway Safety Goals and
Objectives?
Our Goal
Fatalities & Injuries
What is a Beautiful
Road?
A Road with
Fatalities & Injuries
Strategic Planning
Where are we now?
Where do we want to be in the future?
How do we get there?
How do we measure our progress?
The Four E’s of Traffic SafetyThe Four E’s of Traffic Safety
ENGINEERING EDUCATION ENFORCEMENT EMERGENCY
RESPONSE
EngineeringEngineering
Our Safety Emphasis Areas
Young and Senior Drivers
Speeding Drivers
Impaired Drivers
Pedestrian Safety
Bicyclist Safety
Motorcycle Safety
Distracted and Aggressive Drivers
Work zone Safety
Engineering
And
Enforcement
Engineering
And
Enforcement
We Must Focus on
Changing Drivers and
Road User Behavior
We Must Focus on
Changing Drivers and
Road User Behavior
When is Enforcement Effective?
The Six Week Concept
Enforcement changes behavior for up to 6 weeks
Behavior will return without additional enforcement
Engineering and education needed for permanent
change
EducationEducation
Emergency Medical
Services (EMS)
Emergency Medical
Services (EMS)
2009
Manual on
Uniform
Traffic
Control
Devices
Traffic Control Devices
Signs
Signals
Pavement Markings
Traffic Control Devices are used:
Regulatory
Warning
Guide
Multi-Way Stop Sign
Stop Sign shall not be used as traffic
Calming
Must be based on Engineering Study
Five or more reported Crashes in a 12-
month period that are susceptible to
correction by a multi-way stop sign
The Minimum Vehicular volume entering
the intersection total of both
approaches(300 per hour for any 8 hours
of an average day)
Trees
Traffic Calming ProgramTraffic Calming Program
ObjectivesObjectives
Improve neighborhood livability
Enhance safety for residents and street users
Support a multi-modal transportation
system
Roundabouts
Safety Performance –
Contributing Factors
–Reduced speeds
–Reduced decisions
–Reduced conflict points
–Reduced conflict
severity
Urban Single-Lane Roundabout
Bend, OR
Photo: Oregon DOT
Safety Performance
90-100% reduction in fatalities
75% reduction in injuries
35% reduction in total crashes
Very little reported pedestrian and bicycle
crash experience
Source: NCHRP Report 572: Roundabouts in the United States
Cul-de-sacs
The Concept of Preventive
Maintenance
Excellent
Failed
$1.00 for preventive
maintenance here . . .
Delays spending
$4.00 to $5.00 on
more extensive
treatments here.
Age
Co
n
d
i
t
i
o
n
Pedestrian Safety,
Speed Management
and
Bicycle Facility Design
Pedestrian Fatalities (USA)
6,374 Pedestrian Fatalities in 2018
A Pedestrian was Killed Every 1.4 Hours
48% Involved Alcohol (Driver or Pedestrian)
Pedestrian Fatalities Highest since 1990
Pedestrian Fatalities in
Relation to Location (USA)
82% of Pedestrian Fatalities occur
outside of Intersections
Oregon Summary of Pedestrian
Related Traffic Fatalities, 2018
Total Pedestrian Fatalities 79
Total Pedestrian Injuries 949
Percent of Total Oregon Fatalities 15.7%
Source: ODOT
Pedestrian Crash Type:
Dart/Dash
Pedestrian Crash Type - Multiple
Threat/Trapped
Pedestrian Crash Type - Walking Along
Roadway
In most cases, marked crosswalks are best
used in combination with other treatments:
Curb extensions
Raised crossing islands
Traffic signals
Roadway narrowing
Enhanced overhead lighting
Traffic calming measures
Think of the marked crosswalks as one option
in a progression of design treatments
Pedestrian DesignPedestrian Design
shortens crossing distance
What the pedestrian sees
Textured brick complemented with stripes
This area was recaptured from a 4th travel lane; the
street took on a whole new life
Designing for Pedestrian Safety – Road Diets
Portland OR
Shoulder Bikeway
5’ min. against curb or guardrail; 4’ min. open shoulder
Neighborhood
Speed Watch Program
Speed display reader board
Neighborhood speed watch
Issuing warning letters
Law Enforcement
officers must focus on
changing drivers
behavior during the
traffic stop
WELCOME TO
THE CITY OF
ASHLAND
WELCOME TO
THE CITY OF
MEDFORD
The Handbook is available in electronic
form online and can be downloaded at:
www.up.edu/highwaysafety
Thank you for being such
a wonderful audience and for
your commitment to traffic
safety!
For Additional
Information, please contact
Mojie Takallou, Ph.D., P.E.
University of Portland
Tel: 503-943-7437
Email: takallou@up.edu
Website:
www.up.edu/highwaysafety
1
Scott Fleury
From:City of Ashland, Oregon <administration@ashland.or.us>
Sent:Tuesday, April 06, 2021 10:51 PM
To:Scott Fleury; Taina Glick
Subject:Transportation Commission Contact Form Submitted
[EXTERNAL SENDER]
*** FORM FIELD DATA***
Full Name: Louise Shawkat
Phone:
Email: louise40208@gmail.com
Subject: Idling
Message: I would ;Ike the trans commission look into the feasibility of installing a no-idling sign in front of=f city hall-
for obvious health reasons.
Attachment 1 file:
Attachment 2 file:
Attachment 3 file:
*** USER INFORMATION ***
SubscriberID: -1
SubscriberUserName:
SubscriberEmail:
RemoteAddress: 66.241.70.76
RemoteHost: 66.241.70.76
RemoteUser:
#*
#*
#*
#*
#*March 2021 Accidents
Motor Vehicle (6)
#*Bike/Ped Involved (0)
Previous 2021 Accidents
Motor Vehicle (22)
#*Bike/Ped Involved (5)
Traffic CrashesMarch 2021
NO. OF ACCIDENTS:
Rep DATE TIME DAY LOCATION NO.
VEH
PED
INV.
BIKE
INV.INJ. DUII Cited
Police
On Site
PROP
DAM.
HIT/
RUN
CITY
VEH.CAUSE - DRIVER ERROR
Rep 4 16:34 Thur Wightman near Lee St 3 N N NN N YYNNDv1 crashed into 2 parked vehicles within a
block causing damage.
Rep 6 21:20 Sat B Street 2 N N N U N Y Y Y N
Vehicle was struck and damaged while
parked, Wsitness described Dark SUV that
struck parked vehicle
Rep 6 21:21 Sat B Street Near N Pioneer St 2 N N N U N Y Y Y N Vehicle was struck and damaged while parked
Rep 14 11:49 Sun Hersey @ Oak 2 N N N N Y Y U N N
DV2 took wide left turn into intersection and hit
V1. V1 Cited for improper left turn.
Rep 16 15:00 Tue Fairview St 2 N N N N N Y Y N N
D2 opened door into lane while V1 was
passing. V1 hit door.
Rep 24 12:01 Wed Wimer St near Chestnut St 1 N N P Y Y Y Y N N
D1 missed the turn onto Chestnut and
totaled car when hit curb
MONTH: MARCH
MOTOR VEHICLE CRASH SUMMARY
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\5. 20 Is Plenty Program (April 2021 Staff Report).doc
Memo
Date:
April 8, 2021
From: Scott A. Fleury
To: Transportation Commission
RE: 20 Is Plenty-Program Development
BACKGROUND:
The Commission has previously discussed the 20 Is Plenty program report at the January 21,
2021 Meeting. The report and staff information are attached for reference (executive summary).
As part of the Transportation Commission’s charge to recommend Capital Improvement Projects
(CIP) for the Transportation Network to the Director of Public Works, the Commission
recommended including some monies in the CIP to support a 20 Is Plenty Program. Staff has
included $25k in each year of the proposed 2021-2023 Budget Biennium to support financial
needs associated with traffic safety education and outreach, signage, markings, etc. for the
program itself, if it moves forward through a defined process and the City Council approves an
ordinance that lowers residential speed limits by 5mph as allowed by the ORS. If the program
does not receive community support and subsequently Council support through ordinance
approval, then no monies will be spent as part of the program itself.
Primary to the Commission discussion on this item is now developing a programmatic process to
move forward with project development, public engagement and Council interactions. Staff has
included some background information regarding the City of Eugene’s Zero Vision program for
reference as well. Staff has also attached a memo that outlines regulations associated with speed
zones (ORS 810.180).
There appears to be some misconception about the overall scope of the project intent. The 20 Is
Plenty Program’s primary focus per the ORS is for residential roads within the City of Ashland.
The arterials and collector roadway speed limits would not be altered (Hersey Street, North
Main, East Main, Siskiyou Boulevard, Oak Street, & Ashland Street).
Discussion Questions:
1. Next Steps
a. Public outreach/input process
i. Engage Ashland-website tool
1. Survey Information/Public Feedback/Questions
2. Develop Maps-visual breakdown
b. Include in TSP update
c. Standalone components
d. Public Hearing-Transportation Commission
e. Public Hearing-City Council
i. Resolution - Vision Zero (Eugene)
ii. Ordinance – Speed reduction ordinance
2. Pilot projects vs full scale implementation
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\5. 20 Is Plenty Program (April 2021 Staff Report).doc
3. Enforcement capabilities
a. Police outreach
4. Implementation and associated education
a. Process and materials
5. Monitoring parameters
a. Monitor or not
6. Enhancements/changes
CONCLUSION:
Commission should discuss associated next steps/actions for the “20 is Plenty” program.
Evidence Demonstrating the Efficiency, Safety and Economic Benefits of
Reduced Maximum Speed Limits - In Ashland, Oregon
Executive Summary
Oregon state government gives cities some flexibility in setting maximum speed limits (Oregon Revised
Statutes 810.180). Ashland could take advantage of the law and, at the same time, make our community
a better place to live and visit.
Reducing maximum speeds within Ashland will serve to:
• Reduce the incidence and severity of motor vehicle collisions
• Improve safety – especially for people walking and bicycling
• Increase mode share of bicycling and walking
• Reduce carbon emissions
• Reduce consumption of gasoline and expenditures on transportation by Ashland households
• Improve human health
• Reduce vehicle miles of travel
• Lower costs for road maintenance
• Improve social equity
• Increase the potential to attract remote workers (economic development)
• Reduce neighborhood noise
Only three of the above benefits can be monetized. But if Ashland were to make a commitment to lower
maximum speed limits in a manner consistent with ORS 810.180 and enforce those speed limits
adequately, it would stand to generate annual economic savings of more than $1 million. The estimated
implementation cost is approximately $100,000.
Benefits Summary
Category / Source Estimated Annual
Benefits
Reduced incidence and severity of accidents $764,212
Fuel savings (arising from mode shift) $305,554
Carbon emissions reduction $133,758
TOTAL BENEFITS $1,203,524
The cost of changing speed limit signs, adding additional signage as required by Oregon law, and
conducting related speed studies is estimated at $100,000, a one-time expense. Clearly, lower speed
limits will slow the rate of travel. But the additional time that a slower maximum speed adds to a
person’s travel time is measured in seconds. This is a small price to pay for saving lives, money, and the
planet.
Reduced Incidence and Severity of Accidents
Slower speeds allow drivers more reaction time. Additionally, if a crash does happen at slower speeds, it
is much less likely to result in serious injury or death. Traffic deaths do happen in Ashland. In fact, during
the previous five years there have been two deaths. That is two too many. Ashland should ensure that
there are zero deaths or serious injuries. Our community can achieve that outcome by lowering
maximum speeds.
People walking and bicycling are vulnerable road users. If hit by an automobile, they often suffer serious
injury. In fact, 14 percent of accidents in the City involved a pedestrian or person riding a bicycle. Lower
speeds will, as noted above, reduce the number of collisions, and also the severity of injury. Children
and seniors suffer more serious injuries when struck by an automobile (especially when hit by an SUV or
pickup truck). In the hilly parts of town, above Siskiyou Boulevard and N. Main, most streets lack
sidewalks. This means that people walking, bicycling and driving share the same space. If we want
Ashland’s transportation system to be safe for all ages and abilities (and all modes of travel), then
lowering the speed limits will help achieve that outcome.
Ashland Households Can Save Money with Lower Speeds
Some members of our community either don’t drive or don’t own an automobile. Reducing speeds
makes bicycling and walking more practical and safer. When people choose to walk or ride a bike rather
than drive, they pocket the money that they would otherwise spend on gasoline and car maintenance.
These savings add up. Reducing maximum speeds helps to make our community more equitable.
It is estimated, with slower maximum speeds, that about five percent of existing travel using
automobiles would, in the future, be made by people walking or riding a bicycle. The actual shift in
mode may be higher or lower than the forecast. But it is known, based upon surveys in other
communities, that roughly half of Ashland residents are “interested in bicycling but are concerned” for
their safety. They are afraid to share the roadway with motor vehicles traveling at current speeds.
Below is a typical distribution of attitudes toward bicycling. Most people who currently bicycle are
probably either “strong & fearless” or “enthused and confident.”
More people will choose to walk or bicycle if the City’s streets can be adjusted to make it safer for
people to walk or bicycle.
Saving the Planet – Reduced Carbon Emissions
Did you know that every gallon of gasoline you consume produces 20 pounds of carbon dioxide?
Incredible but true. Ashland could, with lower speeds, reduce its carbon dioxide emissions by 1,070
metric tons per year. These emissions reductions occur as a result of more people making the choice to
bicycle or walk rather than drive, for some or all of their trips. That’s right, we can reduce the impact on
the planet by making it safer and more practical for people to choose to walk or bicycle. It’s that simple.
These reductions are equivalent to:
• 836 or 12 percent of Ashland households, who heat water with natural gas, changing out their
existing water heater to a heat pump water heater – at an approximate cost of $2,507,958 or,
• 710 or 10 percent of Ashland households, who heat with natural gas, to convert their natural gas
furnace to a heat pump – at an approximate cost of $4,260,053
Conclusion
We recommend that the City Council:
i. Direct the Public Works Department to pursue reducing maximum speeds within the City to the
maximum extent allowed by Oregon Revised Statute 810.180, and
ii. Request that the Southern Oregon legislative delegation ensure that Ashland is included among
the jurisdictions which would be empowered, as Portland currently is, to set speed limits on
roadways under the City’s jurisdiction pursuant to a reintroduced HB 4103 (2020 legislative
number).
To review the full report (45 pages) see:
https://drive.google.com/file/d/1kmwcUB4CzoAceW4UMZZFu2JzkBVTYPEJ/view?usp=sharing
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\7. ORS 810.doc
Memo
Date:
April 8, 2021
From: Scott A. Fleury
To: Transportation Commission
RE: ORS 810
BACKGROUND:
810.180 Designation of maximum speeds; rules. (1) As used in this section:
(a) “Designated speed” means the speed that is designated by a road authority as the
maximum permissible speed for a highway and that may be different from the statutory speed for
the highway.
(b) “Statutory speed” means the speed that is established as a speed limit under ORS
811.111, or is established as the speed the exceeding of which is prima facie evidence of
violation of the basic speed rule under ORS 811.105.
(2)(a) A designated speed established under this section is a speed limit if the highway for
which the speed is designated is subject to a statutory speed limit under ORS 811.111 that is in
addition to the speed limit established under ORS 811.111 (1)(b).
(b) A speed greater than a designated speed established under this section is prima facie
evidence of violation of the basic speed rule if the designated speed is established for a highway
on which there is no speed limit other than the limit established under ORS 811.111 (1)(b).
(3) The Department of Transportation may establish by rule designated speeds on any
specified section of interstate highway if the department determines that speed limits established
under ORS 811.111 (1) are greater or less than is reasonable or safe under the conditions that
exist with respect to that section of the interstate highway. Designated speeds established under
this subsection are subject to all of the following:
(a) The department may not establish a designated speed under this subsection of more than:
(A) Sixty-five miles per hour for vehicles described in ORS 811.111 (1)(b); and
(B) Seventy miles per hour for all other vehicles.
(b) If the department establishes designated speeds under this subsection that are greater than
65 miles per hour, the designated speed for vehicles described in ORS 811.111 (1)(b) must be at
least five miles per hour lower than the designated speed for all other vehicles on the specified
section of interstate highway.
(c) The department may establish a designated speed under this subsection only if an
engineering and traffic investigation indicates that the statutory speed for the interstate highway
is greater or less than is reasonable or safe under conditions the department finds to exist.
(d) A designated speed established under this subsection is effective when appropriate signs
giving notice of the designated speed are posted on the section of interstate highway where the
designated speed is imposed.
(4)(a) The department may establish, pursuant to a process established by rule, a designated
speed on a state highway outside of a city. The authority granted under this subsection includes,
but is not limited to, the authority to establish different designated speeds for different kinds or
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\7. ORS 810.doc
classes of vehicles as the department determines reasonable and safe. A designated speed
established under this subsection for any kind or class of vehicles may not exceed the speed limit
for the highway for that kind or class of vehicles as established in ORS 811.111 or, if there is no
speed limit for the highway other than the limit established in ORS 811.111 (1)(b), may not
exceed 55 miles per hour.
(b) The department may establish a designated speed under this subsection only if an
engineering and traffic investigation indicates that the statutory speed for the highway is greater
or less than is reasonable or safe under conditions the department finds to exist.
(c) A designated speed established under this subsection is effective when appropriate signs
giving notice of the designated speed are posted on the portion of highway where the designated
speed is imposed.
(5) After a written request is received from a road authority for a highway other than a
highway described in subsection (3) or (4) of this section, the department, pursuant to a process
established by rule, may establish a designated speed for the highway. The authority granted
under this subsection includes, but is not limited to, the authority to establish different designated
speeds for different kinds or classes of vehicles as the department determines reasonable and
safe. The authority granted under this subsection is subject to all of the following:
(a) The written request from the road authority must state a recommended designated speed.
(b) The department may establish a designated speed under this subsection only if an
engineering and traffic investigation indicates that the statutory speed for the highway is greater
or less than is reasonable or safe under conditions the department finds to exist.
(c) The department may not make a final decision to establish a designated speed under this
subsection without providing the affected road authorities with notice and opportunity for a
hearing.
(d) A road authority may file a written objection to a designated speed that is proposed by the
department under this subsection and that affects the road authority.
(e) A designated speed established under this subsection is effective when appropriate signs
giving notice of the designated speed are posted on the portion of the highway where the
designated speed is imposed. The expense of erecting any sign under this subsection shall be
borne by the road authority having jurisdiction over the portion of the highway where the
designated speed is imposed.
(f) The department, pursuant to a process established by rule, may delegate its authority
under this subsection with respect to highways that are low volume or unpaved to a city or
county with jurisdiction over the highway. The department shall delegate authority under this
paragraph only if it determines that the city or county will exercise the authority according to
criteria adopted by the department.
(6) The department may override the speed limit established for ocean shores under ORS
811.111 (1)(c) and establish a designated speed of less than 25 miles per hour on any specified
section of ocean shore if the department determines that the speed limit established under ORS
811.111 (1)(c) is greater than is reasonable or safe under the conditions that exist with respect to
that part of the ocean shore. The authority granted under this subsection is subject to all of the
following:
(a) The department may make the determination required under this subsection only on the
basis of an investigation.
(b) A designated speed established under this subsection is effective when posted upon
appropriate fixed or variable signs on the portion of ocean shore where the designated speed is
imposed.
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(7) A road authority may adopt a designated speed to regulate the speed of vehicles in parks
under the jurisdiction of the road authority. A road authority regulating the speed of vehicles
under this subsection shall post and maintain signs at all park entrances to give notice of any
designated speed.
(8) A road authority may establish by ordinance or order a temporary designated speed for
highways in its jurisdiction that is lower than the statutory speed. A temporary designated speed
may be established under this subsection if, in the judgment of the road authority, the temporary
designated speed is necessary to protect any portion of the highway from being unduly damaged,
or to protect the safety of the public and workers when temporary conditions such as
construction or maintenance activities constitute a danger. The following apply to the authority
granted under this subsection:
(a) Statutory speeds may be overridden by a temporary designated speed only:
(A) For a specific period of time for all vehicles; or
(B) For a specified period of time for a specific kind or class of vehicle that is causing
identified damage to highways.
(b) This subsection may not be used to establish a permanent designated speed.
(c) The authority granted by this subsection may be exercised only if the ordinance or order
that imposes the temporary designated speed:
(A) Specifies the hazard, damage or other condition requiring the temporary designated
speed; and
(B) Is effective only for a specified time that corresponds to the hazard, damage or other
condition specified.
(d) A temporary designated speed imposed under this subsection must be imposed by a
proper written ordinance or order. A sign giving notice of the temporary designated speed must
be posted at each end of the portion of highway where the temporary designated speed is
imposed and at such other places on the highway as may be necessary to inform the public. The
temporary designated speed shall be effective when signs giving notice of the temporary
designated speed are posted.
(9) A road authority may establish an emergency speed on any highway under the
jurisdiction of the road authority that is different from the existing speed on the highway. The
authority granted under this subsection is subject to all of the following:
(a) A speed established under this subsection is effective when appropriate signs giving
notice thereof are posted upon the highway or portion of highway where the emergency speed is
imposed. All signs posted under this subsection must comply with ORS 810.200.
(b) The expense of posting any sign under this subsection shall be borne by the road authority
having jurisdiction over the highway or portion of highway where the emergency speed is
imposed.
(c) A speed established under this subsection may be effective for not more than 120 days.
(10) A road authority may establish by ordinance a designated speed for a highway
under the jurisdiction of the road authority that is five miles per hour lower than the
statutory speed. The following apply to the authority granted under this subsection:
(a) The highway is located in a residence district.
(b) The statutory speed may be overridden by a designated speed only if:
(A) The road authority determines that the highway has an average volume of fewer
than 2,000 motor vehicles per day, more than 85 percent of which are traveling less than 30
miles per hour; and
(B) There is a traffic control device on the highway that indicates the presence of
pedestrians or bicyclists.
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\7. ORS 810.doc
(c) The road authority shall post a sign giving notice of the designated speed at each end
of the portion of highway where the designated speed is imposed and at such other places
on the highway as may be necessary to inform the public. The designated speed shall be
effective when signs giving notice of the designated speed are posted.
(11) A city may establish by ordinance a designated speed for a highway under the
jurisdiction of the city that is five miles per hour lower than the statutory speed. The
following apply to the authority granted under this subsection:
(a) The highway is located in a residence district.
(b) The highway is not an arterial highway.
(c) The city shall post a sign giving notice of the designated speed at each end of the
portion of highway where the designated speed is imposed and at such other places on the
highway as may be necessary to inform the public. The designated speed shall be effective
when signs giving notice of the designated speed are posted.
(12) Notwithstanding ORS 801.430, as used in subsection (11) of this section, “residence
district” includes territory not comprising a business district that is contiguous to a highway and
has access to dwellings provided by alleys. [1983 c.338 §162; 1985 c.16 §51; 1987 c.887 §8;
1989 c.592 §3; 1991 c.728 §3; 1993 c.742 §118; 1995 c.79 §371; 1997 c.249 §227; 1999 c.59
§240; 2003 c.819 §2; 2005 c.77 §1; 2005 c.507 §1; 2011 c.384 §1; 2017 c.291 §1; 2019 c.515
§1]
CONCLUSION:
No action required as this is an informational update to Commission.
EUGENE CITY COUNCIL
AGENDA ITEM SUMMARY
Action: Ordinance Lowering Speeds on Residential Streets
Meeting Date: June 22, 2020 Agenda Item Number: X
Department: Public Works Staff Contact: Chris Henry
www.eugene-or.gov Contact Telephone Number: 541-682-4959
ISSUE STATEMENT
The Eugene City Council will consider action on a proposed ordinance to lower the speed limit to
20 miles per hour on local streets in residential areas.
BACKGROUND
Senate Bill 558 passed in the 2019 legislative session allowing cities to designate speeds on non-
arterial residential streets five miles per hour lower than the statutory speed limit. Effectively, this
legislative change allows the City Council to adopt an ordinance to designate a speed limit of 20
miles per hour on residential non-arterial streets. Previously, only the City of Portland had
authority through legislation to designate speed in this context. The passing of SB 558 extended
authority to all cities.
The Eugene Vision Zero Action Plan, adopted by administrative order in 2019, identified: speed as
a factor in crashes causing injuries or death; slowing vehicle speeds as a strategy to reduce harm
to people in a crash; and taking actions to reduce speed limits on the Vision Zero High Crash
Network as well as supporting legislation to allow local control to designate speed limits.
Speed is a significant determinant of outcomes in crashes. When speeds are lower, people are less
likely to be injured or killed. As speed in a crash increases, the likelihood of serious injury or death
is much greater. Research shows that in crashes at 20 miles per hour, there is a 10 percent
likelihood of serious injury or death; at 30 miles per hour, the likelihood of serious injury or death
increases to 40 percent; and at 40 miles per hour, the likelihood of serious injury or death
increases to 70 percent.
Funding to implement a change designating 20 miles per hour speed limits on local residential
streets has been identified. A strategy for implementation would include changing existing speed
limit signs on local residential streets. The sign changes would include a community outreach
campaign to remind neighbors that “20 IS PLENTY” or similar messaging as used in Portland,
Oregon and other cities around the world. Designating lower speeds on residential collector
streets would be considered on a case by case basis through evaluation of the street context and
presented to Council at a future date as needed.
Efforts to reduce transportation related fatalities or serious injuries are consistent with a triple
bottom line approach to decision-making in the community. Reducing harm to people supports a
healthy community. Making streets safer for people who walk, use a mobility device, ride a bicycle
or the bus makes those options more comfortable and attractive compared to driving a car and is
consistent with climate recovery goals. Reducing crashes and the financial costs of harm to people
or property also supports a healthy economy.
PREVIOUS COUNCIL DIRECTION
On June 15, 2020, the Eugene City Council held a public hearing where they heard comment on an
ordinance to designate lower speed limits on residential streets.
https://eugene.ompnetwork.org/sessions/148248?embedInPoint=2156&embedOutPoint=8677&
shareMethod=link
On March 11, 2020, the Eugene City Council held a work session where they were briefed on
options to adopt an ordinance to designate lower speed limits on residential streets.
https://eugene.ompnetwork.org/sessions/130343?embedInPoint=196980&embedOutPoint=473
7&shareMethod=link
On November 18, 2015, the Eugene City Council adopted Resolution 5143, “A resolution setting as
official policy the Vision Zero goal that no loss of life or serious injury on our transportation
system is acceptable.” https://www.eugene-or.gov/DocumentCenter/View/27858
Following this policy direction, staff provided active legislative support through letter submission
and public hearing testimony (by then City Engineer Matt Rodrigues) to pass SB 558 allowing
cities to designate lower speeds on residential streets. The concept for this bill was one of the top
five priorities for the 2019 legislative session for the League of Oregon Cities – Transportation
Policy Committee on which, City of Eugene Transportation Planning Manager Rob Inerfeld, serves
and Councilor Evans was the chair.
COUNCIL OPTIONS
A. Adopt the ordinance as proposed.
B. Adopt the ordinance with modifications as determined by the City Council.
C. Take no action on the ordinance.
CITY MANAGER’S RECOMMENDATION
The City Manager recommends adopting the ordinance as proposed.
SUGGESTED MOTION
I move to adopt the Ordinance Reducing The Speed Limit To 20 Miles Per Hour On Local Streets In
Residential Areas.
ATTACHMENTS
A. AN ORDINANCE REDUCING THE SPEED LIMIT TO 20 MILES PER HOUR ON LOCAL STREETS IN
RESIDENTIAL AREAS
FOR MORE INFORMATION
Staff Contact: Chris Henry, Traffic Operations Manager
Telephone: 541-682-4959
Staff E-Mail: CHenry@eugene-or.gov
COUNCIL ORDINANCE NO. 20636
AN ORDINANCE REDUCING THE SPEED LIMIT TO 20 MILES PER HOUR ON LOCAL STREETS IN RESIDENTIAL AREAS.
ADOPTED: July 13, 2020
SIGNED: July 20, 2020
PASSED: 8:0
REJECTED:
OPPOSED:
EFFECTIVE: August 20, 2020
Ordinance -- Page 1 of 2
ORDINANCE NO. 20636
AN ORDINANCE REDUCING THE SPEED LIMIT TO 20 MILES PER HOUR ON
LOCAL STREETS IN RESIDENTIAL AREAS.
The City Council of the City of Eugene finds as follows:
A. Through its passage of Resolution No. 5143 on November 18, 2015, the City
Council set as official City policy Vision Zero’s goal of zero fatalities or serious injuries on our
transportation system and directed the City Manager to initiate the formation of a Vision Zero Task
Force.
B. On March 29, 2019, the City Manager issued Administrative Order No. 58-19-04
adopting Eugene’s Vision Zero Action Plan as a guide for action to reach the goal of zero deaths
and life-changing injuries on Eugene's transportation system by 2035. The Vision Zero Action
Plan includes the following Actions:
• SD-9: Work with ODOT to lower speed limits on the Vision Zero High Crash
Network, accompany speed limit changes with street design changes and
enforcement, when possible.
• SD-10: Support legislation to allow local City control to designate speed limits.
C. The Eugene 2035 Transportation System Plan, adopted by City Council on June
26, 2017, includes the following Goal and System Wide Policy:
• Goal 4: Address the transportation needs and safety of all travelers, including
people of all ages, abilities, races, ethnicities, and incomes. Through transportation
investments, respond to the needs of system users, be context sensitive, and
distribute the benefits and impacts of transportation decisions fairly throughout the
City.
• System-Wide Policy 2: Consider safety first when making transportation decisions.
Strive for zero transportation-related fatalities and severe injuries by reducing the
number and severity of crashes through design, operations, maintenance,
education, and enforcement. In furtherance of the City Council’s adopted Vision
Zero goal (Resolution No. 5143), prioritize safety improvements for people who
walk, bike and use mobility devices because no loss of life or serious injury on our
streets is acceptable.
D. Research shows that a person struck by a driver at 25 miles per hour (“MPH”) is
nearly twice as likely to result in death than a crash at 20 MPH (AAA Foundation for Traffic Safety,
Impact Speed and a Pedestrian’s Risk of Severe Injury or Death, 2011).
E. The 2019 Oregon Legislature passed SB 558, amending ORS 810.180 to
authorize cities to designate by ordinance a speed that is five MPH lower than statutory speed on
non-arterial streets in “residence districts” under their jurisdiction.
2018–2023
Vision Zero Action Plan
one traffic death is too many
THIS PAGE INTENTIONALLY LEFT BLANK
ii
EUGENE VISION ZERO
iii
EUGENE VISION ZERO
Acknowledgments
Lane Council of Governments Staff
Bill Clingman
City of Eugene Project Team Staff
Rob Inerfeld
Matt Rodrigues
Larisa Varela
Toole Design
Parents of Traffic Victims
Karen Creighton
Marina Hajek
Susan Minor
Oregon Department of Transportation,
Transportation Safety Division
Chris Ellison 4J Eugene School District
Carmel Snyder Tilford (Ray) Snyder* AARP Oregon
Marina Hajek Advocate for Safe Streets
Pat McGillivray Bethel School District
Rob Zako
Michele O’Leary* Better Eugene-Springfield Transportation (BEST)
Kelsey Moore City of Eugene
Active Transportation Committee
Joe Zaludek City of Eugene, Fire and Emergency Medical
Services Department
Greg Gill City of Eugene Municipal Court
Chief Pete Kerns
Sean McGann* City of Eugene Police Department
Kurt Corey Mark Schoening* City of Eugene Public Works
Brian Johnson
Lane County Public Health
David Reesor Lane County Public Works
Eugene Organ Erycka Organ* Lane Independent Living Alliance
Carl Yeh
AJ Jackson* Lane Transit District (LTD)
Bill Johnston Oregon Department of Transportation (ODOT)
Rick Hamilton Oregon Department of State Police
Matt Roberts University of Oregon
Steve Wildish Randy Hledik* Mary Lou Wilson* Wildish Companies
* Served as Task Force alternate
Sarah Mazze 4J Eugene School District
Bob Beals Bethel School District
JoAnna Kamppi
City of Eugene, Fire and Emergency Management Services
James Hadley City of Eugene
Police Department
Shawn Marsh City of Eugene Police Department
Sean McGann
City of Eugene Police Department
Matt Rodrigues City of Eugene Public Works
Lindsay Selser City of Eugene Public Works
Larisa Varela City of Eugene
Public Works
Steve Dobrinich Lane Council of Governments
Ellen Currier Lane Council of
Governments
Becky Taylor Lane County
Kelly Hoell Lane Transit District
Jake McCallum Lane Transit District
Theresa Brand Point2Point Solutions at Lane Transit District
Nicole Charlson Oregon Department of Transportation
Cheri Kimball Oregon Driver Training
Institute
James Miller Jim Cole Ethan Lodwig PeaceHealth
Task Force Members Technical Advisory Committee Members
Better Eugene-Springfield Transportation (BEST)
iv
EUGENE VISION ZERO
WE PLEDGE,
as the Vision Zero Leadership Committee, to incorporate the Vision Zero Action Plan tenets,
strategies, actions and values into everything our departments do. We commit to work
together in pursuit of the goal of zero traffic-related fatalities and life-changing injuries in the
city of Eugene by 2035.
Chief Chris Skinner
Eugene Police Department
Chief Joseph Zaludek
Fire & Emergency Management Department
Sarah Medary, Executive Director
Public Works Department
Jon Ruiz, City Manager
City of Eugene
v
EUGENE VISION ZERO
Table of Contents
Executive Summary
Why Vision Zero, Why Now? vi
Where to Focus vi
Taking Action vii
In Remembrance viii
Why is Vision Zero Needed? 1
Why Now? 2
What is Vision Zero? 3
Vision Zero Eugene 5
Vision Statement 6
Guiding Tenets 6
This plan is Eugene’s. 7
Causes of deaths and life-changing injuries 8
Crash Causes 9
Strategies to address deaths and life-changing injuries 16
Strategies 17
Locations of deaths and life-changing injuries 20
Where We Need to Focus 21
The Vision Zero High Crash Network 21
Communities of Concern 29
Actions to prevent deaths and life-changing injuries 30
Taking Action 31
Street Design 32
Impairment 34
Dangerous Behaviors 35
Engagement and Accountability 37
Measuring Our Progress 39
Appendix 40
vi
EUGENE VISION ZERO
EXECUTIVE SUMMARY
Vision Zero is an approach to transportation safety that aims to eliminate deaths and life-
changing injuries caused by traffic crashes. In November of 2015, the Eugene City Council
adopted as City policy the Vision Zero goal of zero fatalities and serious injuries on Eugene’s
transportation system as a result of community advocacy efforts. The resolution adopting
Vision Zero directed the City Manager to initiate formation of a Vision Zero Task Force to
develop an Action Plan. This Eugene Vision Zero Action Plan is the result of that direction.
This Action Plan lays out an ambitious set of two- and five-year actions to reach the goal of
zero deaths and life-changing injuries on Eugene’s transportation system by 2035. These
actions will be undertaken by numerous City departments (Public Works, Police, Fire &
Emergency Medical Services), in cooperation with other agency partners such as school
districts and Lane Transit District, and community partners like the University of Oregon and
PeaceHealth.
Vision Zero will also need the ongoing support and commitment of all of Eugene’s residents
who use our streets to walk, bike, roll, take transit and drive. Achieving Vision Zero requires
a true culture change—from one where lives lost or severely harmed is an accepted daily
occurrence to one where deaths and life-changing injuries are unacceptable outcomes of
simply using our streets.
Why Vision Zero, Why Now?
Eugene needs Vision Zero now because people continue
to die and suffer life-changing injuries on our streets. From
2007 to 2015, 60 people were killed and 364 experienced life-
changing injuries as the result of crashes. These numbers
do not reflect the full toll on our community; each victim’s family, friends, coworkers and
acquaintances are also impacted by the loss of someone they knew and loved.
Nearly half of these fatal and life-changing injury crashes affect people walking, biking or riding
a motorcycle, even though these modes make up less than 20 percent of trips in Eugene.
Speed is a critical factor in determining the severity of a crash, and people traveling by these
modes are more vulnerable to greater harm from automobiles, even at relatively low speeds.
Even when speeding is not indicated to be a cause of a crash, the normal driving speed of a
street can contribute to a fatal or life-changing injury. This is also true for people in crashes
only involving cars. Vision Zero aims to improve safety for everyone who uses our streets.
Where to Focus
The development of this Plan included an analysis of crash data from the last nine years
to objectively identify problem streets and intersections. We found that only 9% of streets
account for 70% of fatal and life-changing injury crashes in Eugene. These streets comprise
the Vision Zero High Crash Network and are the focus of many actions in our plan.
vii
EUGENE VISION ZERO
Taking Action
The City of Eugene has been working for years to make its streets safer for all who use them.
This plan builds on that work, as well as a regional and county safety plan and a statewide
plan from the Oregon Department of Transportation.
To reduce crashes that result in deaths and life-changing injuries, the Task Force, Technical
Advisory Committee and staff identified actions to be undertaken that will change policies,
practices and programs and further shift the culture around transportation safety. Development
and implementation of these actions are guided by three tenets to ensure they adhere to the
Vision Zero commitment and important Eugene values: equity, data and accountability.
The actions center around the following four areas that impact the likelihood and severity of
crashes: street design, impairment, dangerous behaviors and engagement and accountability.
While this is our five-year plan to reach Vision Zero, we know this effort will take longer.
In five years, we will review our actions and progress to update this plan to ensure we are
continuing on the path to zero deaths and life-changing injuries. We urge you to join us in this
commitment to making our city’s streets safer for all who travel in Eugene.
Street Design
Actions in this area will impact how
Eugene streets are built and re-designed
in order to improve safety for all people
who use our streets.
Impairment
Actions in this area aim to decrease the
number of people driving, biking and
walking under the influence of alcohol,
marijuana and illicit drugs.
Dangerous Behaviors
Actions in this area upgrade the
enforcement of existing laws, call for
additional, more equitable enforcement,
and aim to change travel behavior
through messaging.
Engagement and Accountability
Actions in this area will keep the Vision
Zero effort on the forefront of City staff
and the community, and will enhance
community engagement in making
Eugene’s streets safer.
viii
EUGENE VISION ZERO
In Remembrance
This plan is dedicated to those who have lost loved
ones and who have had their lives significantly
impacted by traffic crashes. Your losses motivate
us to strive toward a safer Eugene.
1
EUGENE VISION ZERO
The City of Eugene has adopted the bold vision of eliminating traffic deaths and life-changing
injuries on the City’s transportation system. This path toward Vision Zero was adopted by the
Eugene City Council in November 2015, after working with community stakeholders. This is
our community’s story, and our plan to reach zero.
Why is Vision Zero Needed?
The City of Eugene is consistently ranked as one of America’s best places to live. As a small
city with an engaged and inclusive community, thriving universities and surrounding natural
beauty, we agree.
But even though Eugene is highly regarded in some ways, we lose too many members of
our community to traffic crashes on our streets. From 2007 to 2015, 60 people were killed
in traffic crashes in Eugene, and another 364 people sustained life-changing injuries. On
average, someone is killed or experiences life changing injuries every eight days while
traveling on our streets. Traffic crashes are so routine that we are all too often numb to the
toll they take—despite the tragic effect on our families, community and economy.
Not one of us would find it acceptable for a family member, friend, or colleague to be injured
or killed. And the good news is—we don’t have to accept this as fate. Crashes are not
accidents, they are preventable—with the right actions and commitment. Moreover, those
crashes that may still occur do not need to result in deaths or life-changing injuries because
Vision Zero is committed to reducing the severity of crashes so that crashes don’t result in
death or life changing injuries.
Everyone has the right to safely travel on our streets no matter where they are going and how
they travel. That is why the City of Eugene has chosen zero as our goal. By committing to
eliminate traffic deaths and life-changing injuries by 2035 through a Vision Zero program, we
will create a safer and more vibrant city for decades to come.
Eugene Vision Zero Task Force.
2
EUGENE VISION ZERO
Why Now?
Safety has been on the City of Eugene’s
radar for years, but despite everyday efforts
to create safer streets that work well for all
users, investments in transit, and advances in
technology, we still experience an unacceptable
number of traffic deaths and life-changing
injuries. Vision Zero is the next step to
effectively reduce deaths and life-changing
injuries on our streets.
While over 90 percent of crashes involve
only drivers and their passengers, people
walking, biking and riding motorcycles are
disproportionately likely to be seriously injured
or killed on our streets—an unacceptable
inequality for a city that prides itself on healthy,
active, accessible transportation options.
Drivers and their passengers are also in danger.
Over 57 percent of all fatal or life-changing
injury crashes involve drivers and their
passengers.
These data suggest that a bold intervention
and strong commitment from City leaders and
partners—including residents—will be required
to eliminate deaths and life changing injuries.
The Eugene Vision Zero Action Plan establishes
a roadmap for the City to eliminate traffic
deaths and life-changing injuries on its streets.
It signals a shift in transportation engineering
and planning practice to prioritize safety of our
residents over the convenience of traveling
quickly through our city. These changes
won’t always be easy, but we are committed
to working together to achieve them, and
motivated by our determination to create a safe
Eugene for all of our residents and visitors.
Crashes Share
92%
4%2%2%
Fatal and Life-Changing
Injury Crashes
Share
57%13%
16%
14%
City of Eugene, 2007-2015 crash data
Commuter
Mode Share
74%
8%
8%
4%
6%Work from home
3
EUGENE VISION ZERO
What is Vision Zero?
Vision Zero is a transportation safety philosophy that was developed in Sweden in the late
1990s to eliminate traffic deaths and serious injuries in the transportation system. Sweden
already had a significantly lower crash rate than the United States. Even with a low crash rate,
Sweden was still able to work beyond the low hanging fruit and reduce traffic fatalities by
half over 20 years. Sweden is now one of the safest places to travel in the world. By contrast,
traffic fatalities in the U.S. have dropped by only 30 percent over the same time period. If we
would have adopted Vision Zero at the same time as Sweden, over 15,000 lives could have
been saved in the US in 2015 alone.
Vision Zero calls on us to think differently about traffic safety, and to reach beyond traditional
silos to work together for a truly worthy outcome: the elimination of traffic deaths and life-
changing injuries on our streets. Central to Vision Zero is the idea that people should not be
killed or experience life-changing injuries as a consequence of simply using our streets.
Vision Zero recognizes that we all make mistakes, and that the transportation system should
be designed to minimize the impacts of those errors. When crashes do occur, they should
not result in death or life-changing injuries.
In the past five years, over 30 U.S. cities, including Eugene, have adopted Vision Zero goals.
Many have developed detailed action plans to eliminate traffic deaths. While each city
has adapted the program to its own unique needs and situation, the Vision Zero approach
is helping ensure that improving traffic safety is focused on the most powerful tools, like
wholesale speed reduction. Addressing issues of equity has also emerged as a critical
component of Vision Zero initiatives.
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Traffic Safety in the United States and Sweden, 1995-2015
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USA - Actual
Sweden
If the USA had
followed Sweden
4
EUGENE VISION ZERO
A Vision Zero City meets the following minimum standards:
-
- Visi
- Key are engaged.
Vision Zero City
Portland
San Francisco
San JoseFremont
San Diego
Boston
Somerville
New York City
Washington, D.C.Montgomery County
Denver
Chicago
Sacramento
Updated January 2018
Fort Lauderdale
San Antonio
Los Angeles
Columbia
Anchorage
Eugene
Bellevue
San Luis Obispo
Monterey
Bethlehem
Philadelphia
Alexandria Richmond
Cambridge
SantaBarbara
Durham
Macon
Minneapolis
Boulder
Orlando
Vision Zero Cities
Updated January 2018
Source: http://visionzeronetwork.org/resources/vision-zero-cities
Eugene is one of the first small American cities to develop an action plan. We look
forward to helping blaze a path toward safer transportation systems for cities of our size.
The federal government and most states, including the Oregon Department of
Transportation (ODOT), have also established a goal of eliminating traffic deaths. ODOT’s
Transportation Safety Action Plan shares our 2035 goal year for eliminating deaths and
life-changing injuries on the transportation system. Regionally, Lane County Government,
Lane Transit District and the Central Lane Metropolitan Planning Organization are pursuing
similar goals, providing additional support for this initiative.
5
EUGENE VISION ZERO
Václav Hajek, age 10
Václav was a wonderful and kind human being, and
was just a child when he was killed. He blessed
this world with his smile. Václav loved the arts,
outdoors, spending time with family and riding his
bicycle. He wanted to be an artist like his mom.
Václav was killed at age 10 when he was hit by a
speeding teenage driver while walking across the
street with his bike.
Fundamental Principles of
a Meaningful Vision Zero
Commitment
These principles are core to
successful Vision Zero efforts:
1. Traffic deaths and severe injuries are
acknowledged to be preventable.
2. Human life and health are prioritized
within all aspects of transportation
systems.
3. Acknowledgment that human error
is inevitable, and transportation
systems should be forgiving.
4. Safety work should focus on
systems-level changes above
influencing individual behavior.
5. Speed is recognized and prioritized
as the fundamental factor in crash
severity.
(Source: Vision Zero Network)
Vision Zero Eugene
Vision Zero Eugene is truly a collaborative
undertaking. The time is ripe for this
type of systemic change to happen in
our community. As Vision Zero gained
prominence as a new way of thinking about
transportation safety, our community leaders
and professionals united around the need for
change in Eugene. This resulted in the City
Council’s adoption of a Vision Zero resolution
in November of 2015, placing Eugene in
the company of other Vision Zero cities
worldwide.
Since that time, the City has worked closely
with community advocates, policymakers,
and transportation, public health, and law
enforcement professionals to create this
Action Plan. The ultimate goal of the Plan is
to stem the loss of life and opportunity that
occurs via traffic crashes on our streets.
Our Vision Zero Task Force and Technical
Advisory Committee, composed of City
leadership, agency staff and community
stakeholders, have studied the factors
contributing to traffic crashes in Eugene
and debated appropriate, bold actions to address them. We are proud to present our
proposed strategies and actions to you in this plan, and look forward to working with you, our
community, to achieve this vision.
6
EUGENE VISION ZERO
Vision Statement
Our community values the safety of all people who
use our multimodal transportation system and will
take equitable, data-driven actions to eliminate
deaths and life-changing injuries by 2035.
Guiding Tenets
These tenets have helped shape the actions
included in this plan and will continue to guide their
implementation. In order to be effective, every agency,
community partner and resident involved in making
Vision Zero a reality should check their actions against
these tenets by asking is this program or this project
equitable, data-driven and accountable?
Equitable
Eugene strives to be a community where every person regardless of their identity
is safe, valued and welcome. A person’s identity encompasses multiple aspects,
including, but not limited to: age, race, ethnicity, gender, national origin, religion, disability,
sexual orientation, socio-economic and housing status. This plan seeks to make Eugene’s
streets safer for all people who travel on them.
Data-Driven
The actions in this plan were determined through a process of data analysis and
community conversation that took place at community events over the spring,
summer, and fall of 2017. Starting with data allows us to address the issues we know have
caused crashes in the past, and the locations with the worst crash histories.
This plan calls for increasing the amount of data and agency coordination to ensure even
more detailed and pertinent analysis can drive Vision Zero actions in the future.
Accountable
This plan belongs to the people of Eugene. We recognize that traffic crashes
can impact anyone in our community, and we want to be held accountable by our residents
for addressing that serious problem.
To do this, the plan must first be accountable to itself which is why we call for tracking the
effectiveness of actions and making changes where and when needed. When that process
is in place, we can then report to our residents on our successes and adjustments. Progress
will be communicated in an annual report.
Life-Changing Injuries:
Crashes that result in major
injuries change the life of
the victim and their family
and friends. Serious injuries
are defined as those that
prevent the victim from
going about their daily life as
before. This can mean lost
time at work, dependence
on a family member for care,
large medical bills and other
long-term impacts. Though
not fatal, these crashes have
long-lasting effects.
7
EUGENE VISION ZERO
This Plan is Eugene’s
The Vision Zero Resolution was adopted by Eugene City Council after members of the
community raised awareness of the toll that traffic crashes can take on members of our
community, their families and friends. They asked the City Council to strongly articulate that
no traffic deaths or serious injuries are acceptable in our city.
The Vision Zero Resolution specifically directed the City Manager to convene a Task Force to
develop a Vision Zero Action Plan. This plan was guided by a Task Force of city leaders and
community partners who care deeply about the safety of Eugene residents on our streets.
The Task Force met throughout the development of this plan, offering thoughtful, critical
feedback about its direction from the perspective of advocates, implementers and those
whose lives have been changed by traffic crashes.
Achieving Vision Zero will take the whole community to be conscious of their individual
actions and the influence we have on each other. The Vision Zero Action Plan was
collaboratively developed by members of the Eugene community. Opportunities for
engagement included:
• Six Vision Zero Task Force Meetings
• Five Vision Zero Technical Advisory Committee Meetings
• Vision Zero webpages, https://www.eugene-or.gov/VisionZero, that included Task Force
meeting materials and the draft Vision Zero Action Plan
• A public open house for review and comment on the draft Action Plan
• A focus group aimed to elevate Communities of Concern
• Presentations at Neighborhood Association Meetings
• Tabling at community events
• Eugene City Council presentation
Through these public involvement activities, the City provided community members with a
variety of forums to share their concerns and identify priorities for transportation safety.
Moving forward, this plan will remain
Eugene’s. Eugenians will shape
what Vision Zero looks like in their
community. Residents and the
traffic safety behavior they model
for others will continue to play a
critical role in reaching Vision Zero.
In order to reach Vision Zero, we
must all lead by example by being
respectful and considerate users of
our transportation system. The City
of Eugene will partner with other
agencies and our community to reach
Vision Zero.
You are important to helping Eugene end traffic deaths and life-changing injuries on our streets.
You can help by taking the Vision Zero pledge.
I pledge to:
drive and bike sober.
slow down and drive the speed limit.
yield the right of way.
focus on the street and not drive, walk or bike distracted.
share the responsibility of keeping myself, my family, my
friends and my community members safe on our streets.
Thank you for making Eugene’s streets safe for everybody!
Remember, it’s a CRASH, not an “accident.” Traffic deaths ARE preventable. Fir
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During the summer of 2017, hundreds of community members
took the Vision Zero pledge.
8
EUGENE VISION ZERO
CAUSES of deaths and
life-changing injuries
9
EUGENE VISION ZERO
Crash Causes
An extensive data analysis of the City’s crashes from 2007-2015 revealed the following major
contributors to life-changing and fatal traffic crashes in Eugene:
• Street Design
• Dangerous Behaviors
• Impairment
The Vision Zero Action Plan lays out a series of actions intended to address each of these
contributing factors, leading to a safer Eugene for all.
Street Design
A key aspect of Vision Zero is to design streets that are forgiving. While we each have a
responsibility to behave safely on our streets, mistakes happen—and the result cannot be a
life-changing injury or death while moving in and around our city.
Unfortunately, our streets are not always designed with this principle in mind. This is
particularly true for our arterials, on which an astonishing 65 percent of fatal and life-
changing injury crashes occur in Eugene. Many arterials are built for carrying large amounts
of fast-moving automobile traffic. Most have sidewalks, but infrequent safe crossings for
people walking. Some also have transit service, which increases the need for crossing the
street when walking to the bus. And some have bicycle facilities, but they may not be the
safest design for the speed and volume of auto traffic on that street. However, it is not just
people biking and walking who are disproportionately at risk when traveling along these
streets. Despite being built for motor vehicle throughput, still 64 percent of life-changing
and fatal motorist and motorcyclist crashes occur on our arterials. We cannot escape the
conclusion that our arterials must be designed differently to save lives in Eugene.
Crashes vs. Fatalities and Injuries
Data we report here all relate to fatal and life-changing injury crashes. A crash is the
occurrence when parties collide on the transportation system. Fatalities and injuries are
the outcomes of crashes. When a fatality occurs, we consider that a fatal crash, even if
there are also injuries to other people involved. We count the number of crashes rather
than outcomes for two reasons: 1) outcomes would often over-count motor vehicle
crashes where multiple people are killed or injured versus pedestrian and bicycle crashes
where the person walking or biking is most often the only injury or fatality, and 2) the
number of passengers in a car is somewhat random and it can skew counts as well.
10
EUGENE VISION ZERO
minor collector
collector
major collector
minor arterial
major arterial
local
Roadway Classification
Legend
Major Arterial
Minor Arterial
Collector
Local
65% of fatal and life-changing
injury crashes occur on Eugene’s
arterial streets
11
EUGENE VISION ZERO
The designated speed of our streets also needs to be addressed. Crash data state that 12
percent of fatal and serious injury crashes are related to speeding. Yet legal speeds often
result in situations where lives are lost or permanently altered when a crash occurs, and
those instances are not flagged as crashes where speed was a factor. People walking,
biking and driving are more likely to be killed or experience life-changing injuries on 35
mph streets than any other speed in Eugene.1
Surprisingly, however, it is not just our higher-speed streets that are problematic: nearly 1
in 3 of the fatal and serious injury crashes in Eugene occur on streets signed at 25 and 30
mph.2 These data indicate the need for a fundamental rethinking of speed and strategies to
maintain people’s ability to move around Eugene.
The data also indicate that darkness is overrepresented as a contributing factor to crashes.
This is particularly true for people walking, who are about twice as likely to be involved in a
crash during darkness as other travelers. People walking are also more likely to be killed or
experience life-changing injuries in these crashes: approximately 40 percent of fatal and life-
changing crashes involving a person walking occurs in darkness, as compared to less than
25 percent for all other travelers. Travel patterns of people walking suggest that this risk is
likely even greater than the statistics show.
A key part of improving safety is to address our high crash intersections, and to identify
common features between those intersections that we can proactively address at other
locations throughout the network. For example, we know that left turns are overrepresented
among fatal and serious injury crashes in Eugene. Thus, changes to the way our intersections
are designed for turning vehicles may be a critical way of addressing traffic danger in
Eugene.
1 Approximately 36 percent of fatal and life-changing crashes across all modes occurred on streets posted at 35 mph.
2 This figure includes crashes for all modes occurring on streets or at the intersection of two streets posted at 25 or 30 mph.
6040
20
0
80
100
12020
MPH
10%Likelihood of fatality or severe injury
6040
20
0
80
100
12030
MPH
40%Likelihood of fatality or severe injury
6040
20
0
80
100
12040
MPH
70%Likelihood of fatality or severe injury
Source: Tefft, Brian C. Impact speed and a pedestrian’s risk of severe injury or death. Accident Analysis & Prevention. 50. 2013
Likelihood of Death & Severe Injury Due to Speed
12
EUGENE VISION ZERO
Anatomy of a Dangerous Street
No Street Lighting
Fast Moving Traffic
Wide Street
Unprotected Bike Lanes
Unprotected Crossings
Long Distance Between Signals
Before
More Marked CrosswalksShorter distances between safe crossings
Buffered Bike LanesPhysical separation where possible
Safer Speed LimitSafe speeds save lives
More Street LightsEasier to see other people
Safer CrosswalksMore flashing lights, stop lights and median islands
Raised Center MedianSafer turns for people driving
Shorter Crossing Distances at CrosswalksShortened with center median and protected bike lanes
Complete sidewalksAll gaps filled
Speed Safety CamerasSafe speeds save lives
After
Images courtesy of the City of Portland
13
EUGENE VISION ZERO
Dangerous Behaviors
Vision Zero data analysis revealed that a few behaviors seem to be related to many fatal
and life-changing injury crashes in Eugene. Failure to yield is the most common contributing
factor overall and for each mode, clearly indicating a high-priority need to be addressed for
Vision Zero.
Reckless and careless driving, as well as disregarding traffic controls are commonly
associated with fatal and life-changing injury crashes for drivers and motorcyclists.
Additionally, although people walking and biking are less likely to be involved in crashes
involving reckless and careless driving overall, they are highly likely to be seriously injured or
killed when they are involved.
Furthermore, while the state of Oregon has made tremendous progress toward seatbelt
usage over the last few decades, still over 1 in 10 fatal or life-changing injury motor vehicle
crashes in the state involved an unbelted driver or passenger. Similarly, 1 in 14 fatal or life-
changing injury motorcycle crashes may have not resulted in such severity if the motorcyclist
had been wearing a helmet.
Failure to Yield
50%
40%
30%
20%
10%
0%
Speeding Reckless/ Careless Driving Motorcyclist no helmet No seatbelt
S Pedestrian S Bicyclist S Motorcyclist S Driver/Passenger
Top Contributing Factors to Fatal and Life-Changing Injury Crashes in Eugene
City of Eugene, 2007-2015 crash data
14
EUGENE VISION ZERO
We see people driving while using
their phones every day. However,
distracted driving is difficult to
capture via crash statistics, as officers have not
been allowed to cite distraction without seeing
it firsthand. A recent study using broad cell
phone data found that distracted driving was
involved in over 50 percent of trips nationally
that resulted in a crash.3 Starting October 1,
2017, Oregon House Bill 2597 enabled Oregon
police to more actively target distracted drivers,
giving the police the power to cite anyone holding
a mobile device while driving, regardless of
whether they see active talking, texting, and other
use.4 Opportunities for distracted driving are not
likely to go away soon, this legislation provides
a potentially key tool to police to help combat a
persistent problem that is difficult to otherwise
detect. Each of us can also play a key role in
stopping distracted driving. Go to https://www.
eugene-or.gov/VisionZero to take the pledge not
to engage with your mobile phone while driving.
If you need to use your phone, take a moment to
pull over to a safe place away from traffic. It can
wait.
Impairment
Alcohol and drug impairment contributes
substantially to fatalities in Eugene for all modes.
While impairment is a factor in less than five
percent of crashes overall, it plays a part in 50
percent of fatal motorcyclist crashes, 47 percent
of fatal pedestrian crashes, 40 percent of fatal
motorist crashes, and 29% of fatal bicyclist
crashes.
When impairment is involved in a serious or fatal
pedestrian or bicycle crash, pedestrians were found
to be impaired 72 percent of the time, and bicyclists
86 percent of the time. In some crashes, both
3 Cambridge Mobile Telematics, 2017. https://www.cmtelematics.com/press/new-data-cambridge-mobile-telematics-shows-distracted-driving-dangers/
4 The law includes exceptions for those for whom communication is a necessary part of their job. See OR-HB 2597 for more details.
Failure to yield: One or more parties
involved in the crash did not follow
proper yielding law and caused
the crash. Examples of this are
improperly proceeding through an
all-way stop intersection, failing to
yield when turning, or failing to stop
for a pedestrian crossing the street.
Speeding: This category includes
crashes where drivers are cited
either for driving in excess of the
posted speed, or driving too fast
for conditions. The latter occurs
when driving the speed limit is
actually hazardous, such as during
rain or a snowstorm.
Careless driving: Careless driving
is a traffic violation that occurs
when a person “drives any vehicle...
in a manner that endangers or
would be likely to endanger any
person or property.” 1
Reckless driving: Reckless driving
is a Class A misdemeanor and
is charged by the responding
officer when a person “is aware
of and consciously disregards a
substantial and unjustifiable risk
that the result will occur or that the
circumstance exists.” The driver
does not take the same standard
of care that “a reasonable person
would observe in the situation.”2
Disregarding traffic control: A
person who disregards traffic
control fails to stop for any traffic
control device including: a standard
traffic signal, pedestrian hybrid
beacon (such as on Broadway west
of Patterson Street), or stop sign.
1 Oregon Revised Statutes, 811.135
2 Oregon Revised Statutes 161.085.
15
EUGENE VISION ZERO
the person driving and the person walking or
biking was impaired. For crashes only involving
impaired motorists, approximately half involved
only one vehicle in a solo crash, fatally or
seriously injuring the driver. 25 percent of
impaired driver crashes also resulted in a fatal
or life-changing injury for a passenger.
Fortunately, the Eugene Police Department
and the University of Oregon Police
Department are already working to develop
strategies to address impaired driving,
including educational campaigns and
outreach, as well as targeted enforcement.
Additionally, this plan will detail several key
actions we can take both now and in the
coming years to help reduce the influence of
alcohol and drugs on safety in Eugene.
Responding officers have a number of means
of determining whether a driver is under
the influence of alcohol or drugs. “DRE” in
the crash form shown here stands for Drug
Recognition Expert, an officer who has received
training in recognizing drug impairment.
Person
#1 #2
None
Under Influence - Drugs
Under Influence - Alcohol
Under Influence - Meds
Unknown
Determined By:
Intoxilyzer Test
Blood or Urine Test
Field Sobriety Test
Observed (Speech, Odor, Etc.)
DRE Evaluation
Statements
Unknown
Other (Explain)
Results of Test:
P 1_____%P 2_____%
No Test Given
Test Refused
Tested for Drugs
Results Not Available
Impairment section of Oregon state
crash reporting form
Noelle Creighton-Manis, age 23 1/2
Noelle was one of those magical people — high energy,
beautiful, kind, creative and goofy. When she entered a
room, the lights got brighter and laughter increased.
She was gifted with children, dancing, cooking,
and art. Her future included traveling the world and
becoming a pediatrician or pediatric nurse. Noelle
was killed at age 23 ½, the night she was celebrating
her half birthday. She was killed as a passenger in a car
driven by a friend that was drunk and also speeding.
City of Eugene, 2007-2015 crash data
Factors Related
to Fatal Crashes
40%
Alcohol and Drug Related
60%
Other factors
16
EUGENE VISION ZERO
STRATEGIES to address deaths and
life-changing injuries
17
EUGENE VISION ZERO
Strategies
There are six overarching strategies that the City and partners will use to work toward the
goal of zero deaths and life-changing injuries. These strategies address the fundamental
situations that cause crashes, make them more severe, challenge further analysis, and
have the ability to prevent the City from moving as quickly as possible on actions. Actions
presented in this plan grow out of these strategies to address the crash causes (street
design, dangerous behaviors, and impairment) and engagement and accountability.
Strategies are identified in the actions section with an icon.
Reduce potential for conflict between users
Decreasing the possibility that street users can come into conflict is the
first line of defense against crashes. This means providing separated space
for people walking, biking, driving and taking transit along the street. At
intersections, this may mean separating potentially conflicting movements by
time so two parties are not using the same space at the same time.
Slow vehicle speeds
When crashes do occur, they are less serious at slower speeds. Because
not all crashes can be avoided, slowing speeds will decrease the severity
of injuries and lead to fewer fatalities. Increased speed enforcement and
changes to street designs can both help to decrease speeds on Eugene’s
streets. Lowered speed limits may also address speeds but must be paired
with either enforcement or street design, ideally with both.
Reduce driving, bicycling, and walking under the
influence.
Impairment decreases reaction time for all street users and can lead to
poor choices about navigating streets. Impairment stands out as an issue
from additional dangerous behaviors because of its prevalence, its unique
treatments (i.e., human behavior, not engineering solutions), and its influence
on other unsafe behaviors.
Encourage safer practices among people driving,
walking and bicycling
Many crashes result from choices made by street users. Disobeying traffic
controls and laws, driving recklessly, and other behaviors may be best
addressed through culture change in addition to changes to the streets
themselves. Actions in this plan recognize, though, that some unsafe
behaviors, such as walking in the street because there are no sidewalks, are
the result of poor design that does not accommodate people walking.
18
EUGENE VISION ZERO
Improve data collection and analysis
While this plan is the result of a data-driven process, additional analysis will
help further refine and prioritize efforts in the future. Crash data analyzed
in this plan only includes police-reported and citizen-reported crashes to
the ODOT, Oregon Driver and Motor Vehicle Division and thus misses those
crashes when no report was filed. Each report type results in different data
collected, and though most fatal and life-changing injury crashes are reported
by police, we cannot ensure full coverage. Other data limitations regarding
the exact location of crashes(e.g., traveling on a sidewalk versus in the street)
and street design features (e.g., number of lanes) prevent additional analyses
that could tell a more complete story of the crash cause(s).
Support an institutional commitment to Vision Zero
Getting to zero deaths and life-changing injuries requires a major
commitment by the City as a whole. Actions in this category demonstrate
institutional changes that will help Eugene reach its goal. This institutional
commitment can influence residents’ support of the Vision Zero goal and
actions to get to zero deaths and life-changing injuries. Public buy-in will be
necessary to implement many of the actions listed in this plan.
19
EUGENE VISION ZERO
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20
EUGENE VISION ZERO
LOCATIONS of deaths and
life-changing injuries
21
EUGENE VISION ZERO
Where We Need to Focus
Residents of Eugene know that there are streets where more crashes occur than others.
There are streets that are more dangerous for people walking, people biking and people
driving. In many cases, these are the same streets. In order to focus future investments in
safety, City staff have identified and will prioritize streets based on the most recent available
crash data (2007-2015).
Eugene’s most dangerous streets are located throughout the city, crossing through and
dividing neighborhoods from one another. These streets are in residential areas, commercial
districts, downtown and near schools. They are all streets with higher volumes of traffic,
because they connect people to where they need to go. Many of the streets have higher
speed limits, but even some with lower speed limits are the site of higher numbers of fatal
and life-changing injury crashes.
The Vision Zero High Crash Network consists of streets with a higher number of crashes that
result in deaths and serious injuries. There may be other streets in the city with more crashes
that are less severe, but those are not the focus of the Vision Zero effort, saving lives is the
goal.
These streets are the City’s priority locations for making Eugene a safer place to drive, walk
and bike.
The Vision Zero High
Crash Network
The network includes Eugene’s most
dangerous streets and intersections
for people who travel in the city by all
modes – driving, walking and biking.
The Vision Zero High Crash Network
is a compilation pulled from the
top 15 most dangerous streets for
each mode. Fatal and serious injury
crashes for people walking, biking
and driving were mapped individually
to assess which streets were most
dangerous. These lists were then
reviewed with the community
to check public perceptions and
confirm that these streets have
the worst safety issues. During the
implementation phase of this Action
Plan, the City will take a closer look
at crash clusters along the Vision
Zero High Crash Network to focus
transportation safety improvements.
22
EUGENE VISION ZERO
minor collector
collector
major collector
minor arterial
major arterial
I-105
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11TH AVE
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RANDY PAPE BELTLINE
High Crash Network
Source: ODOT crash data, 2007-2015
High crash intersection
High crash street
While the High Crash Network
includes just 9% of Eugene streets,
more than 70% of fatal and life-
changing injury crashes occur on
the High Crash Network.
23
EUGENE VISION ZERO
We understand that the entire length of a street, though included in the Vision Zero High
Crash Network, may not have a concerning history of serious crashes. The City will take on
a corridor approach to reaching Vision Zero, but we are aware that some streets noticeably
change context over their length. The City will focus work on those parts of streets known to
have concerning crash histories or have characteristics similar to areas with higher numbers
of serious crashes.
The cross section of Wilamette Street changes several times over its length from downtown to South Eugene.
24
EUGENE VISION ZERO
High Crash Intersections
In addition to corridors, high crash intersections have also been identified. These are
locations with higher numbers of crashes across all modes. High crash intersections include
places like Roosevelt Boulevard & Highway 99 where two major streets meet in a wide
intersection that also includes bike lanes, transit stops and lots of driveways.
They also include a place like 18th Avenue & Hilyard Street where though there was only
one serious injury crash, there were ten recorded moderate injury crashes for people biking.
Though none of these resulted in a serious injury, this sheer volume of crashes indicates a
safety problem.
Roosevelt Boulevard & Highway 99
18th Avenue & Hilyard Street
25
EUGENE VISION ZERO
These intersections are locations with three or more total crashes, using a weighted
total. Fatal and major injury crashes for all modes are counted as one crash. Moderate
injury crashes for people walking and biking are also counted, but at a weight of 0.5. This
means an intersection with one fatal crash and four moderate injury crashes would be
tallied as three total.
Auto Bike Pedestrian
High Crash Intersection Fatal Serious Injury Fatal Serious Injury Moderate Fatal Serious Injury Moderate
Highway 99 & Roosevelt Boulevard 4 1 1 1
18th Avenue & Hilyard Street 1 9 1
MLK Jr Boulevard & Kinsrow Avenue 4 3
18th Avenue & Willamette Street 2 4 1
11th Avenue & Danebo Avenue 4
River Road & Silver Lane 2 1 1 1
30th Avenue/Amazon Parkway & Hilyard Street 3 1 1
Coburg Road & Oakmont Way 2 4 1
River Road & Hunsaker Lane 1 1 1 1 1
7th Avenue & Jefferson Street 2 1 1
Willamette Street & Brae Burn Drive 3 1
15th Avenue & Alder Street 1 1 3
Crescent Avenue & Gilham Road 3 1
Barger Drive & Terry Street 2 1 1
Northwest Expressway & Beltline (Westbound)3
Highway 99 & Royal Avenue 1 1 1
11th Avenue & Tyinn Street 2 2
River Road & Azalea Drive 3
Highway 99 & 5th Avenue 3
Division Avenue & Lone Oak Avenue 1 1 2
River Road/Chambers Street &
Northwest Expressway 1 4
27th Avenue & Willamette Street 1 3 1
11th Avenue & Alder Street 3
Source: ODOT crash data, 2007-2015
26
EUGENE VISION ZERO
The Vision Zero high crash streets
of people driving includes streets
with the largest number of fatal and
serious injury crashes between 2007
and 2015.
Vision Zero High Crash Streets of People Driving
death life-changing injury
Randy Pape Beltline
Highway 99
11th Ave
Coburg Rd
River Rd
7th Ave
I-105
Delta Highway
Chambers St
Northwest Expressway
Jefferson St
Martin Luther King Jr Blvd
Willamette St
Crescent Ave
Roosevelt Blvd
minor collector
collector
major collector
minor arterial
major arterialHigh Crash Network of People DrivingSource: ODOT crash data, 2007-2015
0 5 10 15 20 25 30 35 40
27
EUGENE VISION ZERO
The Vision Zero high crash streets of
people walking and biking consist of
those streets with the largest number
of fatal, serious injury and moderate
injury crashes. Though the focus of
Vision Zero is to eliminate crashes
resulting in deaths and life-changing
injuries, locations with high numbers
of moderate injury crashes for
vulnerable users are also important.
11th Ave
Willamette Street
18th Ave
7th Ave
River Rd
Hilyard St
Chambers St
13th Ave
8th Ave
Royal Ave
Pearl St
Martin Luther King Jr Blvd
24th Ave
Irvington Dr
Echo Hollow Rd
Vision Zero High Crash Streets of People Walking
death life-changing injury moderate injury
minor collector
collector
major collector
minor arterial
major arterialHigh Crash Network of People WalkingSource: ODOT crash data, 2007-2015
0 5 10 15 20
28
EUGENE VISION ZERO
18th Ave
River Road
Willamette St
11th Ave
13th Ave
Hilyard St
Chambers St
Coburg Rd
Highway 99
Alder St
Pearl St
7th Ave
Royal Ave
15th Ave
6th Ave
Vision Zero High Crash Streets of People Biking
death life-changing injury moderate injury
The difference between a serious
and moderate injury for vulnerable
users—people walking and
biking—can be as little as a vehicle
traveling five miles an hour more.
Additionally, moderate injury
crashes for these modes could be
underreported.
minor collector
collector
major collector
minor arterial
major arterialHigh Crash Network of People BikingSource: ODOT crash data, 2007-2015
0 5 10 15 20 25 30 35 40
29
EUGENE VISION ZERO
Communities of Concern
As a guiding tenant of the Action Plan, equity will help guide implementation of Vision
Zero in Eugene. Identifying Communities of Concern helps the City become more aware of
historically underserved and disadvantaged neighborhoods in Eugene that may need and
deserve more equitable transportation investments. The City acknowledges that there have
been historic disinvestments in some communities over Eugene’s history. Compared to other
neighborhoods, residents living in Communities of Concern may have fewer choices about
how, when and where they move around our City, putting them at a higher risk of danger as
they use our streets.
The City will prioritize transportation safety investments in Communities of Concern. Our
Vision Zero guiding tenets direct that both equity and safety data are used to identify and
prioritize investment.
Upon adoption of this plan, the City will undertake the action of developing a Communities of
Concern map to guide investments.
David Minor, age 27
David Minor was riding his bike when he was struck
and killed by a car while turning at 13th & Willamette.
He enjoyed music, skiing, camping, gardening, and
spending time with his family and many friends.
David was a dreamer, a thinker, a lover of life, and
a passionate believer in social justice. He inspired
those who knew him in the way he lived his life:
championing the rights of all people, cherishing and
nurturing relationships, and being a good steward of the
environment. He was funny and fun, smart and kind, and will
always be missed by those who knew and loved him.
David’s parents are working with the City of Eugene to develop a two-way
protected bike lane on 13th from downtown Eugene to the UO.
30
EUGENE VISION ZERO
ACTIONS to prevent deaths and
life-changing injuries
31
EUGENE VISION ZERO
Taking Action
Vision Zero requires bold action to reach a bold goal.
Eliminating deaths and life-changing injuries on the
transportation system is no small feat. Reaching that goal
will take actions not just from City staff in many different
departments, but also from partners at agencies such as
the Lane Council of Governments (LCOG), Lane Transit
District, Oregon Department of Transportation (ODOT),
school districts, institutions of higher education such as
the University of Oregon, civic groups, as well as Eugene
residents and visitors.
The actions presented here were developed in concert
with partners who will be responsible for helping to
execute and support them. The Task Force and Technical
Advisory Committee for this plan drew from City
departments, partner agencies, institutions and civic
groups. In addition, City staff consulted individually with
departments about actions that they could take and
barriers that currently prevent them from working toward
safer streets. The Plan’s guiding tenets of being data-
driven, equitable and accountable will heavily influence
the implementation of the actions laid out in this plan.
Many actions will be on-going. Changing the design of
the city’s streets is not a one-time step, it must happen
consistently over years redesigning existing streets and
with construction of new streets. Other actions, such as
amending state law regarding speed limit setting, may
take sustained effort, but they will have a concrete end.
Actions are organized into two time frames: two-year and
five-year actions. These categories mean that a given
action will be completed (one-time actions) or started (on-
going actions) within that time frame. Annual reports will
be developed to assess the progress made and success
of these actions.
Reduce potential
for conflict
between users
Slow vehicle
speeds
Reduce driving,
bicycling and
walking under
the influence
Encourage
safer practices
among people
driving, walking
and bicycling
Improve data
collection and
analysis
Support an
institutional
commitment to
Vision Zero
The information gathered in preparation of this plan illuminates the fact that best practices for
the design and regulation of transportation facilities change over time. With more users and a
greater diversity in means of travel on the City’s transportation system, streets that were well
designed when they were created no longer meet our ideals. The plan’s goal is for the “key
implementers” to carry out the identified actions according to the timeframes proposed. It is
important to recognize that City budgetary constraints and changes in political support for
the identified actions may impact the City’s ability to carry out the identified actions within the
identified timeframes. Performance measures are provided to gauge the plan’s progress.
32
EUGENE VISION ZERO
Street Design
Actions in this area will influence the physical design of Eugene’s streets and the process by
which street designs are developed and approved.
Key Implementers
• City of Eugene Department of Fire and Emergency
Medical Services
• City of Eugene Department of Planning and Development
• City of Eugene Department of Public Works
• Lane Council of Governments (LCOG) Safe Lane
Coalition
• Lane County Public Works
• Oregon Department of Transportation
• 4J and Bethel School Districts
Two-year actions
SD-1: Build capital safety infrastructure improvements along the Vision Zero High Crash Network each year. Example: Construction of a median island with additional enhancements to create a safer crossing for pedestrians and/or bicyclists on a wide street.
SD-2: Implement signal phasing and operational changes for the High Crash Intersections each year. Example: Implement signal timing and phasing modifications or upgrades to reduce crashes and improve safety.
SD-3: Use assessment of demonstration or pilot projects as proof of concept for safety infrastructure changes on the Vision Zero high crash streets and intersections.
SD-4: Prioritize street maintenance (surface and striping) on the Vision Zero high crash streets and intersections.
SD-5: Prioritize sidewalk infill, inspection and maintenance of sidewalks on the High Crash Streets for People Walking.
SD-6: Review resurfacing and restriping maintenance projects with the safety of all users in mind.
SD-7: Integrate Vision Zero into the City’s development review checklist to ensure that public rights-of-way are being designed for the most vulnerable users of our streets.
SD-8: Review and revise City code governing site design’s interface with the public right-of-way to incorporate safe design standards for all modes and to prioritize safety along the high crash network. Example: current driveway/access management code (Chapter 7).
The City is already taking
a major step this year
to improve the safety
of its street design with
development and planned
adoption of updated
Street Design Guidelines
that recommend features
for new streets and
retrofits to existing ones.
33
EUGENE VISION ZERO
Two-year actions
SD-9: Work with ODOT to lower speed limits on the Vision Zero High Crash Network,
accompany speed limit changes with street design changes and enforcement, when possible.
SD-10: Support legislation to allow local City control to designate speed limits.
Five-year actions
SD-11: Build a database of information on street design features to enable systemic safety
analysis.
SD-12: Perform systemic safety analysis to determine street factors associated with crash types for each mode.
SD-13: Conduct safety reviews of the transportation network in school areas. Develop education and engineering recommendations to improve safety for all modes of school travel. Agencies will work together to ensure site planning for schools incorporates traffic safety review prior to siting/opening new, reconstructed or relocated school(s).
SD-14: Review and revise Fire & Emergency Medical Services call response procedures for appropriate response vehicle to call type.
34
EUGENE VISION ZERO
Impairment
With the amount of microbrews, marijuana and illicit drugs available in Eugene, driving, biking
or walking under the influence of alcohol or drugs is a temptation that some Eugenians
experience in their daily lives. Fatal and life-changing injury crashes that involve impairment
negatively impact far too many of our community members. Actions in this area include
educational activities, focused increase of enforcement, and institutional changes to make
enforcement more feasible.
The Lane Council of Governments (LCOG) has already convened a workgroup focused on
developing strategies that will reduce driving under the influence throughout our region. The
City of Eugene participates in this effort and will continue to partner with LCOG on many
actions in this area.
Key Implementers
• City of Eugene Police Department
• Lane Transit District
• LCOG Safe Lane Coalition
• Oregon Department of Transportation
• University of Oregon
• Alcohol serving establishments
• Marijuana dispensaries
• Taxi and Transportation Network Companies and their drivers
Two-year actions
I-1: Regularly deploy (year-round) high-visibility DUII enforcement in high-priority areas on nights with higher concentrations of severe and fatal crashes and on days with major community events. Pair enforcement with education.
I-2: Create and routinely deliver collaborative driving, walking, and bicycling under the influence social marketing campaign(s). Time campaign and media slots with holidays or major celebrations that may spur impaired driving, biking and/or walking. Include
enforcement effort notification in campaigns.
I-3: Collaborate and build partnerships with transit, taxi companies, transportation network companies (TNCs) like Uber or Lyft, Oregon Liquor Control Commission, bar owners and dispensaries to reduce driving under the influence, especially targeting hot spot locations.
Five-year actions
I-4: Increase the number of police officers trained as Drug Recognition Experts.
I-5: Support statewide efforts to reform DUII standards related to Blood Alcohol Content, arrest and adjudication process, and repeat offenders.
35
EUGENE VISION ZERO
Dangerous Behaviors
Actions in this area focus on influencing the behavior and attitudes of people driving, walking
and biking in Eugene. Combined, these actions address failure to yield, reckless and careless
driving, speeding and distraction. Public communications about dangerous behaviors will
focus on creating a culture of safety, one where we are all responsible for our own and each
other’s safety on Eugene’s streets.
The City is already addressing additional dangerous behaviors through helmet education and
giveaways for vulnerable users, and participation in the SafeKids car seat program.
Key Implementers
• City of Eugene Department of Fire and
Emergency Medical Services
• City of Eugene Police Department
• City of Eugene Department of Public Works
• Courts
• Lane Transit District
• Oregon Department of Transportation
• SafeKids Coalition
• LCOG Safe Lane Coalition
In 2017, the legislature made legal
the use of red light cameras for
speed enforcement. The City of
Eugene currently does not have
any red light cameras. Legislation
regarding expansion of rights to
install fixed, automated speed
safety cameras is expected in the
2019 long legislative session.
36
EUGENE VISION ZERO
Two-year actions
DB-1: Increase awareness of Vision Zero.
DB-2: Deploy speed reader trailers to increase awareness of speeding and slow vehicle speeds.
DB-3: Install automated enforcement cameras for red light violations on the Vision Zero High Crash Network using a data-driven process Direct revenue generated by traffic citations directly back to the City’s Vision Zero Program.
DB-4: Focus traffic enforcement on the Vision Zero High Crash Network and on behaviors contributing to fatal and serious injury crashes (impaired driving, speeding, failure to yield, aggressive driving, and distracted driving).
DB-5: Develop and implement a social marketing campaign that identifies dangerous behaviors. Include messaging that communicates personal and shared responsibility to keep
our community safe.
DB-6: Support legislation to allow the use of fixed stand-alone, unstaffed speed safety
cameras on the Vision Zero High Crash Network and Intersections in Eugene.
DB-7: Require training on traffic safety and Vision Zero values for all City employees receiving fleet driving permissions. Move toward requirement for defensive driving class.
DB-8: Develop and implement a marketing campaign(s) that promotes defensive skills all
road users can learn to increase safety including, but not limited to, 1) increase knowledge of defensive driving and biking skills that teach people how to anticipate other road users’ potential movements and 2) increase visibility for people walking and biking. Support with education and distribution of safety equipment such as sunglasses, lights and reflectors.
Five-year actions
DB-9: Increase number of Full Time Equivalent (FTE) of Eugene Police Department Patrol
Operations Division so that officers have discretionary time to enforce traffic safety.
DB-10: Increase FTE in order to hire an Injury Prevention Specialist with training in traffic safety for the Fire & Emergency Medical Services Department.
DB-11: Provide transportation options and safety information to residents with suspended licenses at the City of Eugene Community Court and traffic court.
DB-12: Purchase speed reader trailers designed for deployment on arterial streets.
37
EUGENE VISION ZERO
Engagement and Accountability
Engagement and Accountability is an action
area critical to reducing fatal and life-
changing injury crashes in Eugene. The City
cannot achieve Vision Zero on its own—it will
take a community to eliminate all fatalities
and life-changing injuries on our streets.
A key part of our work toward Zero will be
engaging with the community to ensure that
we are aware of our community’s concerns,
tapping into their best ideas, and working
with them to implement these strategies,
leading to our greatest chance of success.
To this end, we are working to secure long-term funding to support both the City and
community groups working together to improve traffic safety through education, engineering
and creative outreach. We are also committed to partnering with other organizations,
including ODOT, our health and emergency services personnel, and our partners in the
educational system to establish strong relationships, which will enable us to work together to
incorporate safety in our culture at every opportunity.
Vision Zero will and must continue to be in the public eye for us all to take our part in the
shared responsibility of making Eugene’s streets safer. Actions in this area will continue to
communicate the importance of getting to zero deaths and life-changing injuries and will
keep Vision Zero visible in the community.
We will also hold ourselves accountable for our goals, as reflected by the performance
measures at the end of this plan. We pledge to update the public on the progress toward our
goals via an annual report, providing our residents with information about how things are
improving, what is coming next, our overall progress toward Zero, and key opportunities to
become involved. We look forward to this journey toward Zero together, as a community.
Actions in this area will keep the Vision Zero effort visible to City staff and the community.
They will also enable direct community engagement in making Eugene’s streets safer.
The City already integrates safety messaging into many public events and campaigns
related to transportation, and this will continue, now linked to the Vision Zero program. New
initiatives like the Oregon Friendly Driver program also demonstrate our existing and on-
going commitment to changing the culture of safety in Eugene.
Key Implementers
• City of Eugene Department of Fire and
Emergency Medical Services
• City of Eugene Police Department (EPD)
• City of Eugene Department of Public Works
• Eugene 4J and Bethel School Districts
• University of Oregon, Lane Community
College and Northwest Christian University
• LCOG Safe Lane Coalition
• PeaceHealth
38
EUGENE VISION ZERO
Two-year actions
EA-1: Create an internal Vision Zero team that meets to review traffic crash data, equity data, and traffic safety performance. Task team members with presenting this data at appropriate meetings (monthly Public Works, Police, and other City meetings).
EA-2: Institutionalize conducting before and after studies of Vision Zero Actions.
EA-3: Develop and provide Vision Zero messaging on an ongoing basis to be delivered at City public, neighborhood group, stakeholder group meetings as well as City media interviews.
EA-4: Work with local colleges/universities to create and implement a new walking, biking,
driving and transit riding safety campaign for students.
EA-5: Provide targeted outreach and training when adding pedestrian or bicycle facilities to teach street users how to navigate a newly constructed facility.
EA-6: Convene a Vision Zero advisory body comprising Task Force and TAC members on a routine basis during the implementation phase.
EA-7: Increase permanent FTE in the Public Works Department to implement Vision Zero.
EA-15: Develop and publish a Communities of Concern map(s) to guide investments.
Five-year actions
EA-8: Coordinate with EMS/trauma center data to understand locations and magnitude of underreporting of crashes.
EA-9: Develop a Street Ambassador program that empowers neighborhoods to develop safety programs, including using the existing City neighborhood grants program, as well as opening the opportunity to affordable housing communities.
EA-10: Work with ODOT Transportation Safety Division and/or school districts to increase
access to driver education for new and young drivers.
EA-11: Work with ODOT to revise crash reporting standards to better inform data analysis. Work with Eugene Police Department to implement new data collection methods.
EA-12: Develop processes and funding opportunities to support the participation of community-based organizations in the development and implementation of Vision Zero-related efforts.
EA-13: Reinstate multimodal high school level transportation safety education programs that includes bicycle and pedestrian safety curriculum as well as transportation options
information.
EA-14: Provide bike/walk safety education to most K-8 students during their time with Eugene
4J and Bethel school districts through the Safe Routes to School Program.
EA-16: Establish a sustainable funding source for Vision Zero infrastructure projects, education efforts and program management.
39
EUGENE VISION ZERO
Measuring Our Progress
Tracking our progress over time is critical to understanding if we’re on pace to meet our
goals, and to helping us pinpoint what we can do better in the future. In this vein, we worked
with the Task Force and Technical Advisory Committee to develop key performance metrics
for our strategies and actions. We also drew from best practices in performance measures
and other key Vision Zero resources to make sure we were holding ourselves to high, yet
realistic standards. Along with our partners, we will monitor our progress and produce an
annual report for the public.
Our overall goal is to reach zero fatalities and life-changing injuries by 2035. To measure
progress toward this goal, we will monitor the number of people killed or seriously injured on
the transportation system. On the way to our 2035 goal, we will aim to decrease deaths and
life-changing injuries by 25 percent by 2023 and 50 percent by 2028, using a rolling average.
An update to the Vision Zero Action Plan will begin in 2023 as we near completion of the two-
and five-year goals laid out in this plan.
The following tables demonstrate how we propose to measure our progress toward each of
the actions, in support of our overall goal.
40
EUGENE VISION ZERO
APPENDIXPerformance
Measures
41
EUGENE VISION ZERO
Action Area: Street Design
Action Time-frame Lead Support Performance Measure
Reduce potential for conflicts between users
SD-1
Build capital safety infrastructure improvements along the Vision Zero High Crash Network each year. Example: Construction of a median island with additional enhancements to
create a safer crossing for pedestrians and/or bicyclists on a wide street.
Two-year Public Works
Annually, number and total cost of capital safety improvements built a) along corridor segments and b) at intersections in the Vision Zero High
Crash Network
SD-2
Implement signal phasing and operational changes for the High Crash Intersections each year. Example: Implement signal timing and phasing modifications or upgrades to reduce
crashes and improve safety.
Two-year Public Works
Annually, number of phasing and operational changes implemented
a) along the Vision Zero High Crash Network and b) at Vision Zero High Crash intersections
SD-3
Use assessment of demonstration or pilot projects as proof of concept for
safety infrastructure changes on the Vision Zero high crash streets and intersections.
Two-year Public Works
Annually, whether demonstration or pilot projects were used as proof
of concept for safety infrastructure changes along the Vision Zero Crash Streets and High Crash Intersections
SD-4
Prioritize street maintenance (surface
and striping) on the Vision Zero high crash streets and intersections.Two-year Public Works
Annually, percentage of total street
maintenance that was completed along the Vision Zero High Crash Network
SD-5 Prioritize sidewalk infill, inspection, and maintenance of sidewalks on the High Crash Streets for People Walking.
Two-year Public Works
Annually, linear feet of sidewalk infill constructed, number of sidewalk
inspections and number of sidewalk repairs on the High Crash Streets for People Walking
SD-6
Review resurfacing and restriping
maintenance projects with the safety of all users in mind.Two-year City of Eugene, Public Works Lane County Public Works
Percentage of resurfacing and
restriping projects that address safety of all users
SD-7
Integrate Vision Zero into the City’s development review checklist to ensure
that public rights-of-way are being designed for the most vulnerable users of our streets.
Two-year
Public Works,
Planning & Development
Whether Vision Zero was integrated into the City’s development review checklist
The following performance measures were created to help us understand our progress toward eliminating fatalities
and life-changing injuries in Eugene. There are many actions we need to take over the next few years to help Eugene
reach Zero. Measuring our progress regarding those actions (via output measures looking at what we've done), as
well as how the needle is moving toward zero (via outcome measures looking at the results of our actions) will help
us understand our progress toward the overall goal, as well as provide insight into what is working well and what
needs more support along the way.
42
EUGENE VISION ZERO
Action Time-frame Lead Support Performance Measure
SD-8
Review and revise City code governing
site design’s interface with the public right-of-way to incorporate safe design standards for all modes and to prioritize safety along the high crash network. Example: current driveway/access
management code (Chapter 7).
Two-year Public Works, Planning & Development
a) Whether City code was revised to incorporate safe design standards for all modes; and b) whether safety was explicitly prioritized in proximity to the Vision Zero High Crash Network
Slow vehicle speeds
SD-9
Work with ODOT to lower speed limits
on the Vision Zero High Crash Network, accompany speed limit changes with street design changes and enforcement, when possible.
Two-year Public Works ODOT, EPD
Annually, amount of high-crash segments with reduced speed limits, that received street design changes
and/or enforcement
SD-10 Support legislation to allow local City control to designate speed limits.Two-year City of Eugene, LCOG Safe Lane Coalition
Whether the City of Eugene included local control to establish speed limits as a legislative priority
Improve data collection and analysis
SD-11 Build a database of information on street design features to enable systemic safety analysis.Five-year Public Works Whether a systemic safety database was built
SD-12 Perform systemic safety analysis to determine street factors associated with crash types for each mode.Five-year Public Works Whether a systemic safety analysis was performed
SD-13
Conduct safety reviews of the transportation network in school areas. Develop education and engineering
recommendations to improve safety for all modes of school travel. Agencies will work together to ensure site planning for schools incorporates traffic safety review prior to siting/opening new,
reconstructed or relocated school(s).
Five-year
School Districts, Social Service Providers,
Public Works
UO
Annually, number of school area transportation networks that have received a safety review within the last
five years
SD-14
Review and revise Fire & Emergency Medical Services call response procedures for appropriate response vehicle to call type.
Five-year Fire & EMS EPD
Whether call response procedures for appropriate response vehicle to call type were reviewed and changed in order to help reach Vision Zero
43
EUGENE VISION ZERO
Action Area: Impairment
Action Time-frame Lead Support Performance Measure
Reduce driving, walking, and bicycling under the influence
I-1
Regularly deploy (year-round) high-visibility DUII enforcement in high-priority areas on nights with higher concentrations of severe and fatal crashes and on days with major community events. Pair enforcement with education.
Two-year EPD UO, LCOG,Public Health
1) Annually, percentage of a) nights with higher concentrations of severe and fatal crashes and b) days with major community events with high-visibility DUII enforcement events in high-priority areas; 2) Annually, percentage of DUII enforcement events paired with education;3) Annually, number of DUII citations per 10,000 population4) Biennially, whether EPD received DUII grant funding to support high-visibility programs
I-2
Create and routinely deliver collaborative driving, walking, and bicycling under the influence social marketing campaign(s). Time campaign and media slots with holidays or major celebrations that may spur impaired driving, biking and/or walking. Include enforcement effort notification in campaigns.
Two-year LCOG Safe Lane Coalition, UO
1) Annually, a) number of driving under the influence social marketing campaigns delivered, b) percentage of campaigns that were timed to coincide with holidays or major celebrations, and c) percentage of campaigns including enforcement effort notifications;2) Number of visits to social media website and associated pages
I-3
Collaborate and build partnerships with transit, taxi companies, transportation network companies (TNCs) like Uber or Lyft, Oregon Liquor Control Commission, bar owners and dispensaries to reduce driving under the influence, especially targeting hot spot locations.
Two-year
LCOG Safe Lane Coalition, EPD, local businesses, UO
1. Whether partnerships were built with a) transit, b) taxi companies, c) TNCs, d) bar owners, and e) dispensaries with a focus on reducing driving under the influence, particularly in target locations
Annually, percentage of hot spot locations successfully targeted within the prior three years via partnerships with transit, taxi companies, TNCs, bar owners, and dispensaries for reducing driving under the influence
I-4 Increase the number of police officers trained as Drug Recognition Experts.Five-year EPD Annually, number of police officers trained as DRE.
I-5
Support statewide efforts to reform DUII standards related to Blood Alcohol Content, arrest and adjudication process, and repeat offenders.
Five-year LCOG Safe Lane Coalition
Whether LCOG supported statewide efforts to reform DUII standards related to a) BAC, b) arrest and adjudication process, and c) repeat offenders
44
EUGENE VISION ZERO
Action Area: Dangerous Behaviors
Action Time-frame Lead Support Performance Measure
Slow vehicle speeds
DB-1 Increase awareness of Vision Zero.Two-year Public Works EPD, BEST, LCOG
Biennially, percentage of Eugene residents who take a “traffic safety pledge” and agree to drive without distraction, speeding, or intoxication
DB-2 Deploy speed reader trailers to increase awareness of speeding and slow vehicle speeds.Two-year EPD Public Works
1) Annually, percentage of hours when signs are deployed. 2) Whether recorded speeds decrease over duration of a given deployment
DB-9
Increase number of Full Time Equivalent (FTE) of Eugene Police Department Patrol Operations Division so that officers have discretionary time to enforce traffic safety.
Five-year EPD
Annually, 1) number of FTE of the Eugene Patrol Operations Division, 2) ratio of patrol operations dedicated/undedicated time spent on traffic stops, 3) number of FTE of the Eugene Police Department Traffic Enforcement Unit (TEU) and 4) ratio of dedicated/undedicated TEU time spent on traffic enforcement
DB-12 Purchase speed reader trailers designed for deployment on arterial streets.Five-year EPD, Public Works Number of arterial speed reader boards purchased
Reduce potential for conflicts between users
DB-3
Install automated enforcement cameras for red light violations on the Vision Zero
High Crash Network using a data-driven process. Direct revenue generated by traffic citations directly back to the City’s Vision Zero Program.
Two-year Public Works EPD
1) Annually, percentage of intersections on the Vision Zero High Crash Network identified as having a high number of crashes related to disregarding a traffic signal with automated enforcement cameras for red light violations2) Annually, number of injury crashes occurring at a) each high crash intersection, b) within a quarter-mile radius, stratified by whether or not automated enforcement is present
DB-4
Focus traffic enforcement on the Vision Zero High Crash Network and on behaviors contributing to fatal and serious injury crashes (impaired driving, speeding, failure to yield, aggressive driving, and distracted driving).
Two-year EPD
Annually, 1) Percentage of HCN streets receiving regular traffic enforcement; 2) percentage of tickets pertaining to behaviors contributing to fatal and serious injury crashes (speeding, failure to yield, aggressive driving) versus less serious infractions, both a) overall and b) specifically in the HCN; 3) Percentage of campaigns including enforcement effort notification
45
EUGENE VISION ZERO
Action Time-frame Lead Support Performance Measure
Encourage safer behavior among drivers, pedestrians, and bicyclists
DB-5
Develop and implement a social marketing campaign that identifies dangerous behaviors. Include
messaging that communicates personal and shared responsibility to keep our community safe.
Two-year LCOG Safe Lane
Coalition
1) Whether a social marketing campaign that identifies dangerous behaviors was a) developed, and b) implemented; 2)
whether messaging that communicates personal and shared responsibility to keep community safe was included
DB-6
Support legislation to allow the use of fixed stand-alone, unstaffed speed safety cameras on the Vision Zero High Crash Network and Intersections in Eugene.
Two-year Public Works EPD
Whether City of Eugene supported legislation to allow the use of fixed
automated enforcement cameras for speed violations in Eugene
Support an institutional commitment to Vision Zero
DB-7
Require training on traffic safety and Vision Zero values for all City employees receiving fleet driving permissions.
Move toward requirement for defensive driving class.
Two-year Public Works
1) Whether training on traffic safety and
Vision Zero values for all City employees receiving fleet driving permissions became a requirement; 2) whether participation in a defensive driving class became a requirement
Annually, 1) percentage of City employees with fleet driving
permissions trained on traffic safety and Vision Zero values in the last five years; 2) percentage of City employees with fleet driving permissions who have participated in a defensive driving class
in the last five years
DB-8
Develop and implement a marketing campaign(s) that promotes defensive
skills all road users can learn to increase safety including, but not limited to, 1) increase knowledge of defensive driving and biking skills that teach people how to anticipate other road users’ potential
movements and 2) increase visibility for people walking and biking. Support with education and distribution of safety equipment such as sunglasses, lights and reflectors.
Two-year Public Works EPD
1) Whether a marketing campaign that promotes defensive skills for all road users was a) developed, and b) implemented; 2) whether support was
provided in the form of distributed safety equipment
DB-10
Increase FTE in order to hire an Injury
Prevention Specialist with training in traffic safety for the Fire & Emergency Medical Services Department
Five-year Fire & EMS Whether an injury prevention specialist was hired for Fire and EMS
DB-11
Provide transportation options and safety information to residents with suspended licenses and at City of Eugene Community Court and traffic
court.
Five-year Courts, LTD, DMV
Whether information on transportation options was provided a) to residents with suspended licenses, b) at City of Eugene Community Court, and c) at
traffic court
46
EUGENE VISION ZERO
Engagement and Accountability
Action Time-frame Lead Support Performance Measure
Improve data collection and analysis
EA-1
Create an internal Vision Zero team that meets to review traffic crash data, equity data, and traffic safety performance. Task team members with presenting this data at appropriate meetings (monthly PW, Police, and other City agency meetings).
Two-year Public Works, EPD
1) Number of internal Vision Zero team meetings occurring to review traffic crash data, equity data, and traffic safety performance; 2) number of meetings where team members presented this data (monthly PW, Police, and other City agency meetings)
EA-2 Institutionalize conducting before and after studies of Vision Zero Actions.Two-year Public Works
Percentage of before and after studies of Vision Zero Actions institutionalized, including identification of information necessary to analyze effectiveness of actions
EA-8 Coordinate with EMS/trauma center data to understand locations and magnitude of underreporting of crashes.Five-year Public Works, Fire & EMS, PeaceHealth EPD Whether coordination with EMS/trauma center data was completed
Encourage safer behavior among drivers, pedestrians, and bicyclists
EA-3
Develop and provide Vision Zero
messaging on an ongoing basis to be delivered at City public, neighborhood group and stakeholder group meetings, as well as City media interviews.
Two-year Public Works
Annually, number of City a) public, b) neighborhood group, and c) stakeholder group meetings where Vision Zero
messaging was delivered
EA-4
Work with local colleges/universities to create and implement a new walking, biking, driving and transit riding safety
campaign for students.
Two-year U of O, Lane CC, NW Christian University
Whether the City worked with local
colleges/universities to a) create and b) implement a new walking, biking, driving and transit riding safety campaign for students
EA-5
Provide targeted outreach and training when adding pedestrian or bicycle facilities to teach street users how to
navigate the newly constructed facility.
Two-year Public Works
Whether targeted outreach and training was provided to street users to teach them how to navigate new pedestrian
and bicycle facilities
EA-9
Develop a Street Ambassador program that empowers neighborhoods to
develop safety programs, including using the existing City neighborhood grants program, as well as opening the opportunity to affordable housing communities.
Five-year Public Works CIty Manager's Office
1) Whether a Street Ambassador program that promotes traffic safety in neighborhoods and along high crash corridors was developed; Annually, number of a) neighborhood and b) high
crash corridor traffic safety events run by the volunteer Street Ambassador program
EA-10
Work with ODOT Transportation Safety Division and/or school districts to increase access to driver education for
new and young drivers.
Five-year LCOG Safe Lane Coalition, DMV ODOT Annually, percentage of new and young drivers who participated in a driver education program
47
EUGENE VISION ZERO
Action Time-frame Lead Support Performance Measure
EA-11
Work with ODOT to revise crash reporting standards to better inform data analysis.
Work with EPD to implement new data collection methods.
Five-year Public Works,
EPD ODOT
1) Whether crash reporting standards were revised to better capture data necessary for data analysis; 2) Whether EPD implemented new data collection methods
EA-12
Develop processes and funding opportunities to support the participation of community-based organizations in the development and implementation of Vision Zero-related efforts.
Five-year Public Works Number of community leaders and organizations supported to participate in Vision Zero-related efforts
EA-13
Reinstate multimodal high school
level transportation safety education programs that include bicycle and pedestrian safety curriculum as well as transportation options information.
Five-year Fire & EMS, Lane County, School districts, EPD
Whether high school level transportation safety education programs were revived
EA-14
Provide bike/walk safety education to most K-8 students during their time with Eugene 4J and Bethel school districts through the Safe Routes to School Program.
Five-year Eugene 4J and Bethel School Districts
Point2Point at LTD, City of Eugene Recreation
Percentage of schools providing bike/walk education
Support an institutional commitment to Vision Zero
EA-6
Convene a Vision Zero advisory body comprising Task Force and TAC members on a routine basis during the implementation phase.
Two-year Public Works Annually, number of Vision Zero advisory body meetings
EA-7 Increase permanent FTE in the Public Works Department to implement Vision Zero. Two-year Public Works Whether there is an increase in permanent FTE in Public Works to focus on Vision Zero implementation
EA-15 Develop and publish a Communities of Concern map(s) to guide investments Two-year Public Works Whether a Communities of Concern Map was developed and published
EA-16
Establish a sustainable funding source for Vision Zero infrastructure projects, education efforts and program management.
Five-year Public Works, EPD, Fire & EMS
Annually, number of dollars allocated toward a) Vision Zero infrastructure projects, b) education efforts, and c) program management
www.eugene-or.gov/VisionZero
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
Memo
Date:
April 8, 2021
From: Scott A. Fleury
To: Transportation Commission
RE: City of Ashland Evacuation Planning
BACKGROUND:
At the September and October 2020 Commission meetings, the group discussed the current
evacuation planning project being led by Ashland Fire and Rescue (AFR). Staff provided
information on the project itself including scope of work and kickoff meeting presentational
materials.
The Evacuation Time Estimate (ETE) Study is now essentially complete with a final draft report
provided to the City by KLD. This draft report is being reviewed by associated staff members
within the Fire, Police, Administration, and Public Works Department(s). The final draft report is
attached for reference along with the final meeting presentational materials provided by KLD.
The ETE study breaks down the time necessary to evacuate the emergency management zones
(EMZ) based on wildfire scenarios seasonally. The EMZ’s are defined by the City’s emergency
services (Police & Fire). Figure 1 shows the EMZs for the City of Ashland. Chapter 7 of the
document provides the overview of the ETEs.
To generate an accurate model and determine the ETEs based on the various scenarios KLD
conducted a road link analysis survey of the complete City.
This involved:
a. Field survey of road network by Traffic Engineers
b. GPS Data of the road network
c. Speeds/topography/signal locations/general characteristics
d. Develop detailed computer representation for detailed modeling of ETE
Information on the road link node analysis can be found in Appendix H of the study. Once all the
information was plugged into the model the scenarios were run providing the ETE output data.
The model output is generally conservative as items were run in sequence, not parallel. For
example, if a resident works outside of the community it was calculated that that resident would
commute back to Ashland and then begin preparation to evacuate even if there was already
someone at home who could have started preparation.
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
Figure 1:
The study defined numerous scenarios for modeling ETE for each of the EMZs and
combinations of the EMZs. Table 1 shows the multiple scenarios modeled to develop the ETEs.
Table 1: Evacuation Scenarios
The results of the various modeled scenarios are shown in Table 2 below. The longest ETE is for
the Fall-Midweek-Midday scenario, due to Southern Oregon University school year being in full
swing (3:10 hours for 90% of total population to evacuate). Breakdowns of the maps for all the
EMZ scenarios are shown in Appendix G - Evacuation Regions.
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
Table 2: ETE Summary
In addition to the standard scenarios modeled, KLD also simulated a couple “what if” scenarios.
These included the additional of emergency on-ramps at the North Mountain overpass, the
Nevada Bridge connection and both together. The outcomes of these “what if” scenarios are
shown below in Table 3.
Table 3: “What If” Scenarios
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
General Recommendations:
Access Impaired Neighborhoods
Consideration should be given regarding placement of evacuation signage, in accordance with
the MUTCD (see Section 11.3), along Frank Hill Rd, Ashland Mine Rd, Granite St, Ashland
Loop Rd, Morton St, Terrace St, Elkader St, Highwood Dr, Pinecrest Terrace, Timberlake Dr,
Emigrant Creek Rd, and E Nevada St to guide evacuees out of these neighborhoods.
Coordinate with Southern Oregon University with respect to evacuation preparedness.
Improve mobilization time.
Evacuate in less vehicles.
Education and Outreach.
Previous Background:
The current evacuation planning effort was developed by AFR as they received a State
Homeland Security Grant for an evacuation planning study. Through a formal solicitation
process KLD was selected to perform the study and develop an Evacuation Time Estimate (ETE)
using substantial local information provided by staff. The project is now essentially complete
with the final draft study in the hands of staff for review and comment. Staff received a
presentation from KLD on the study April 5th and are working to finalize comments for KLD
before the study is completely finalized.
KLD Project Approach:
2. Kickoff Meeting (Compete)
a. Discuss assumptions, needs, study area etc.
3. Review existing data and plans
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
a. Emergency Management Plan
b. Adjacent Communities Plans
c. Natural Hazard Mitigation Plan
d. Others
4. Demographic survey (Complete)
a. Online survey
b. Advertisement-CERT/Nixle/Other
5. Road Survey and link-node analysis network (Complete)
a. Field survey of road network by Traffic Engineers
b. GPS Data
c. Speeds/topography/signal locations/general characteristics
d. Develop detailed computer representation for detailed modeling of ETE
6. Access impaired neighborhoods (Complete)
a. Single ingress/egress routes
b. Bottlenecks
c. Recommendations-safe refuge, access and signage
7. Identify regions and scenarios (Complete)
a. Existing area command zones (APD) for evacuation management zones (EMZ)
b. Evacuation of single EMZs and entire EMZ area to be considered
c. Six scenarios for planning purposes
8. Data gathering, analysis and processing (Complete)
a. Facility data (schools, medical, day care, tourist attraction)
b. Transportation resources (buses, ambulances, wheelchair transport, mutual aid,
mobilization time)
c. Plans, studies, etc.
d. GIS information
9. Fuel and weather modeling (Complete)
a. Scenario based fire spread modeling
10. Progress meetings (Complete)
a. Project status check ins
11. Compute evacuation time estimates (ETE) (Complete)
a. Specific modeling of scenarios and EMZs to compute ETE
b. Identify congestion patterns and locate bottlenecks
c. Visual representations
12. Estimate impacts on ETE (Complete)
a. ETC from step 10 and “what if” scenario planning
13. Technical report (Complete)
a. Develop final comprehensive draft technical report
b. Address comments
c. Final report
14. Final meeting (Complete)
a. Final summary presentation of findings
Previous planning and associated documentation:
City of Ashland Emergency Management Plan:
The plan provides a framework for coordinated response and recovery activities during a large-
scale emergency. The plan describes how various agencies and organizations in the City of
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
Ashland will coordinate resources and activities with other Federal, State, local, community- and
faith-based organizations, tribal, and private-sector partners.
https://www.ashland.or.us/Files/AshlandEMP_FullPlan_Final_07312018.pdf
November 18, 2018 Study Session:
Evacuation and Process Planning- Emergencies or disasters, such as earthquakes, floods,
wildfires, or hazardous materials spills, may require the need to evacuate people from hazardous
areas to lower risk zones. In such an event, emergency responders or Emergency Operations
Center personnel may determine that the evacuation of a part or all the city is necessary to
minimize the risk of injury or death. This report is intended to identify the local resources and
capacity to effectively carry out an evacuation process.
Minutes
Staff Report
August 14, 2017 Study Session:
Essential Route and Structure discussion with the City Council.
Minutes
Staff Report
Wildfire Evacuation Route:
Current wildfire evacuation rout map posted on the City’s website
Wildfire Evacuation Route Map
Wildland Fire Action Guide:
Part of the ready set go program and details (planning document).
http://www.ashland.or.us/SIB/files/RSG%20National%20Action%20Guide%2C%20Print%20Re
ady%20w%20crop%20marks.pdf
Ashland Fire and Rescue provides information on developing your own personal evacuation
action plan including template forms that can be downloaded from the City’s website.
Read! Set! Go! Personal Evacuation Action Plan
Level 1
Ready – Be Ready, Be Firewise. Be ready for the potential to evacuate. Be aware of the dangers
in your area by monitoring emergency service websites and local media outlets for information.
Take personal responsibility, prepare your family and belongings so your home is ready to leave.
For wildfire, be Firewise by reducing your home's ignition potential. Assemble emergency
supplies and belongings in a safe place. Create an Evacuation Plan with escape routes and make
sure all those residing within the home know the plan of action. Taking the correct route during
an evacuation is critical for your safety. Tune into information about where to go during an
evacuation, see sources below. Make sure you're registered in Nixle if you want contact in any
way other than a landline telephone. Register for Nixle by clicking here
Level 2
Set – Situational Awareness. Be set to evacuate at a moment’s notice. This level indicates
significant danger in your area and voluntarily relocating to a shelter or family is advisable. This
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
may be the only notice that you receive; emergency services cannot guarantee that they will be
able to reach you again if the condition worsen. If you do decide to stay, pack your emergency
items and be ready to leave at a moment’s notice.
Level 3
Go! – Act Early! Leave immediately and follow your personal evacuation plan. Do not delay
leaving by gathering your belongings or make efforts to save your home. Danger in your areas is
imminent and you should evacuate immediately. If you chose not to evacuate, emergency
services many not be able to assist you. DO NOT plan to return to check on your house or
animals until it's declared safe to do so.
Staying informed during an emergency is critical. These are some of the ways we will relay
information:
Nixle Citizen Alert System: ashland.or.us/nixle
Ashland Emergency Broadcast Station: 1700 AM
Wildfire Information Hotline: 541-552-2490
City of Ashland Website: www.ashland.or.us
Jackson County Emergency Management: www.rvem.orghe
City of Ashland Emergency Management Plan (Link)
CONCLUSION:
No formal action required at this time. The information is for discussion and comment purposes
moving forward. Staff expects there to be additional planning work associated with the ETE
including internal planning for continued education and outreach to the community on
evacuation planning/readiness.
Staff has spoken with Kittelson Associates regarding the information contained within the ETE
and how to incorporate that information into the Transportation System Plan update with respect
to transportation network enhancements that could improve evacuation processes. The updated
scope and fee from Kittelson will be brought before the Commission at the May/June meetings
for review, discussion and recommendation to award a contract for services.
Finally, staff will continue to work with reginal partners to enhance evacuation safety.
G:\pub-wrks\eng\dept-admin\TRANSPORTATION COMMISSION\2021 Staff Memos\April 15, 2021\Packet\12. City of Ashland Evacuation Planning Staff Report.doc
2
4
Wildfire
Reception Center 1
Reception Center 2
EMZ to Reception Center 1
EMZ to Reception Center 2
If you live here,
and…
You travel here to
evacuate the area,
then…
You are at risk until you
leave the area being
evacuated
5
Evacuation travel time depends on the
relationship between Traffic Demand and
Highway Capacity (Supply).
When Demand exceeds Capacity over some time period, travel speeds decline and congestion, exhibited by queuing and stop-and-go conditions, is present.
Traffic does move, but slowly.
6
7
ETE for Special
Facilities
DYNEV-II
DYNAMIC TRAFFIC SIMULATION MODEL
ETE for General
Population (Residents,
Tourists, Employees)
Evacuation Speeds
DEMAND
•Permanent Residents
•Tourists
•Employees
•Special Events
•External Traffic
•Special Facilities
SUPPLY
•Evacuation Routes
•Number of Lanes
•Roadway Capacity
•Traffic Control Devices
•Traffic Control Tactics
Permanent Residents - year-
round population within the
EMZs.
Census data and results from
the demographic survey were
used to compute the number
of residents and evacuating
resident vehicles.
Shadow Evacuees
–those who live
outside of the
EMZs but may
voluntarily
evacuate.
Tourists - those people visiting the
community parks, sporting events,
camps, and other recreational
areas.
Data was provided by the City.
Assumptions were made to fill in
any gaps and to compute vehicle
occupancies.
Employees - those people who work
within the EMZs, but who live outside
the EMZs.
Census data and results from the
demographic survey were used to
compute the number of employees and
employee vehicles.
External Traffic – vehicles
whose origin and destination
are outside the EMZs but
pass through the study area
as part of their trip.
8
Residents Resident
Vehicles Tourists Tourist Vehicles Employees
Employee
Vehicles External Traffic
EMZs 21,449 13,666 5,150 2,252 2,302 2,173 0
Shadow Region 10,099 6,490 0 0 0 0 8,412
Total 31,548 20,156 5,150 2,252 2,302 2,173 8,412
Schools, medical facilities, transit dependent individuals and Southern Oregon University were also considered.
9
10
A link-node analysis network
(computerized representation of
physical roadway system) was
developed.
Link-node analysis network
calibrated using Road Survey (July
2020) observations.
See Appendix H of the report for
detailed maps and entry data for
the link-node analysis network.
11
Scenario Season Day of Week
Time of
Day
1 Summer Midweek Midday
2 Summer Weekend Midday
3 Summer Midweek,
Weekend Evening
4 Fall Midweek Midday
5 Fall Weekend Midday
6 Fall Midweek,
Weekend Evening
12
13
14
0
20
40
60
80
100
0 60 120 180 240
Pe
r
c
e
n
t
of
Po
p
u
l
a
t
i
o
n
Be
g
i
n
n
i
n
g
Ev
a
c
u
a
t
i
o
n
Tr
i
p
Elapsed Time from Evacuation Advisory (min)
Trip Generation Distributions
Employees/Tourists Residents with Commuters Residents with no Commuters
16
Summer Fall
Midweek Weekend
Midweek
Weekend Midweek Weekend
Midweek
Weekend
Scenario:(1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 –EMZ 1 1:55 1:45 1:40 1:55 1:45 1:40
R02 –EMZ 2 1:55 1:50 1:45 1:55 1:50 1:45
R03 –EMZ 3 1:40 1:50 1:45 1:35 1:50 1:45
R04 –EMZ 4 2:00 1:55 1:55 2:00 1:55 1:55
R05 –EMZ 5 1:30 1:25 1:20 1:30 1:30 1:20
R06 –EMZ 6 1:50 1:50 1:50 1:45 1:50 1:50
R07 –EMZ 7 1:50 1:50 1:50 1:50 1:50 1:50
R08 –EMZ 8 1:50 1:55 1:50 1:50 1:55 1:50
R09 –EMZ 9 1:55 1:50 1:50 1:55 1:50 1:50
R10 –EMZ 10 1:55 1:55 1:55 1:55 1:55 1:55
R11 ‐Western Ashland 1:50 1:50 1:45 1:35 1:50 1:45
R12 ‐Eastern Ashland 1:50 1:50 1:50 1:55 1:50 1:50
R13 ‐Northern Ashland 2:00 1:55 1:50 2:00 1:55 1:50
R14 ‐Central Ashland 1:45 1:50 1:45 2:30 1:50 1:45
R15 ‐Southern Ashland 2:05 1:55 1:55 2:45 1:55 1:55
R16 ‐Northern and Central
Ashland 1:55 1:50 1:50 2:30 1:50 1:50
R17 ‐Southern and Central
Ashland 1:55 1:50 1:50 1:50 1:50 1:50
R18 ‐All EMZs 2:35 2:25 2:15 3:10 2:25 2:20
17
Summer Fall
Midweek Weekend
Midweek
Weekend Midweek Weekend
Midweek
Weekend
Scenario:(1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 –EMZ 1 4:00 4:00 4:00 4:00 4:00 4:00
R02 –EMZ 2 4:00 4:00 4:00 4:00 4:00 4:00
R03 –EMZ 3 4:00 4:00 4:00 4:00 4:00 4:00
R04 –EMZ 4 4:00 4:00 4:00 4:00 4:00 4:00
R05 –EMZ 5 4:00 4:00 4:00 4:00 4:00 4:00
R06 –EMZ 6 4:00 4:00 4:00 4:00 4:00 4:00
R07 –EMZ 7 4:00 4:00 4:00 4:00 4:00 4:00
R08 –EMZ 8 4:00 4:00 4:00 4:00 4:00 4:00
R09 –EMZ 9 4:00 4:00 4:00 4:00 4:00 4:00
R10 –EMZ 10 4:00 4:00 4:00 4:00 4:00 4:00
R11 ‐Western Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R12 ‐Eastern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R13 ‐Northern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R14 ‐Central Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R15 ‐Southern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R16 ‐Northern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R17 ‐Southern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R18 ‐All EMZs 4:00 4:00 4:00 4:00 4:00 4:00
Dictated by
trip
mobilization
18
Maximum travel time is approximately 1 hour and 30 minutes.
0%
20%
40%
60%
80%
100%
0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
Pe
r
c
e
n
t
of
To
t
a
l
Ve
h
i
c
l
e
s
Elapsed Time (h:mm)
ETE and Trip Generation
Fall, Midweek, Midday
(Scenario 4)
Trip Generation ETE
Travel Time
Need to identify the region numberNeed to identify the region number
Need to identify the scenario numberNeed to identify the scenario number
What is the ETE for the following set of circumstances?What is the ETE for the following set of circumstances?
It is Wednesday, October 14th at 12:00PM (Fall – school in session, Midweek, Midday).
Wildfire ignited to the north and only threatens the northern areas of Ashland.The 90th percentile ETE is needed.
19
20
Scenario Season Day of Week Time of Day Conditions
1 Summer Midweek Midday Normal
2 Summer/Spring Weekend Midday Normal
3 Summer/Spring Midweek, Weekend Evening Normal
4 Fall Midweek Midday Normal
5 Fall Midweek Midday Reduced Roadway Capacity
6 Fall Weekend Midday Normal
7 Fall Midweek, Weekend Evening Normal
21
Region
Emergency Management Zone (EMZ)
Description 12345678910
R01 EMZ1 X
R02 EMZ2 X
R03 EMZ3 X
R04 EMZ4 X
R05 EMZ5 X
R06 EMZ6 X
R07 EMZ7 X
R08 EMZ8 X
R09 EMZ9 X
R10 EMZ10 X
R11 Western Ashland ‐EMZ1, EMZ2,
EMZ3, EMZ4 XXXX
R12 Eastern Ashland ‐EMZ5, EMZ6,
EMZ7, EMZ8, EMZ9, EMZ10 XXXXXX
R13 Northern Ashland ‐EMZ1, EMZ2,
EMZ10 XX X
R14 Central Ashland ‐EMZ3, EMZ7,
EMZ8, EMZ9 XXXX
R15 Southern Ashland ‐EMZ4, EMZ5,
EMZ6 XXX
R16
Northern and Central Ashland ‐
EMZ1, EMZ2, EMZ3, EMZ7, EMZ8,
EMZ9, EMZ10
XXX XXXX
R17
Southern and Central Ashland ‐
EMZ3, EMZ4, EMZ5, EMZ6, EMZ7,
EMZ8, EMZ9
XXXXXXX
R18 All EMZ XXXXXXXXXX
EMZ(s) Shelter‐in‐Place EMZ(s) Evacuate
Table 7‐1. Time to Clear the Indicated Area of 90 Percent of the Affected Population
22
Summer Fall
Midweek Weekend
Midweek
Weekend Midweek Weekend
Midweek
Weekend
Scenario:(1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 –EMZ 1 1:55 1:45 1:40 1:55 1:45 1:40
R02 –EMZ 2 1:55 1:50 1:45 1:55 1:50 1:45
R03 –EMZ 3 1:40 1:50 1:45 1:35 1:50 1:45
R04 –EMZ 4 2:00 1:55 1:55 2:00 1:55 1:55
R05 –EMZ 5 1:30 1:25 1:20 1:30 1:30 1:20
R06 –EMZ 6 1:50 1:50 1:50 1:45 1:50 1:50
R07 –EMZ 7 1:50 1:50 1:50 1:50 1:50 1:50
R08 –EMZ 8 1:50 1:55 1:50 1:50 1:55 1:50
R09 –EMZ 9 1:55 1:50 1:50 1:55 1:50 1:50
R10 –EMZ 10 1:55 1:55 1:55 1:55 1:55 1:55
R11 ‐Western Ashland 1:50 1:50 1:45 1:35 1:50 1:45
R12 ‐Eastern Ashland 1:50 1:50 1:50 1:55 1:50 1:50
R13 ‐Northern Ashland 2:00 1:55 1:50 2:00 1:55 1:50
R14 ‐Central Ashland 1:45 1:50 1:45 2:30 1:50 1:45
R15 ‐Southern Ashland 2:05 1:55 1:55 2:45 1:55 1:55
R16 ‐Northern and Central
Ashland 1:55 1:50 1:50 2:30 1:50 1:50
R17 ‐Southern and Central
Ashland 1:55 1:50 1:50 1:50 1:50 1:50
R18 ‐All EMZs 2:35 2:25 2:15 3:10 2:25 2:20
24
Legend
EMZ Area
25
27
Note: 2‐4 Ambulances can be made available from Jackson County Fire
Department in the case that more are needed.
Transportation
Resource Buses
Wheelchair
Buses Ambulances
Resources Available
Ashland School District 19 0 0
Ashland Fire & Rescue 0 0 2
Jackson County Fire District 0 0 2‐4
TOTAL: 19 0 2
Resources Needed
Medical Facilities (Table 3‐7):66 32
Schools (Table 3‐10):75 0 0
Transit‐Dependent Population (Table 11‐1):40 0
TOTAL TRANSPORTATION NEEDS: 85 6 32
28
29
Table 9-2. School Evacuation Time
Estimates
Table 9-4. Special Facility Evacuation Time
Estimates
School
Driver
Mobilization
Time (min)
Loading
Time (min)
Dist. To
Safety (mi)
Average
Speed
(mph)
Travel
Time to
Safety
(min)
ETE
(hr:min)
City of Ashland, OR
Helman Elementary School 90 15 2.8 3.7 45 2:30
Ashland High School 90 15 3.2 2.6 74 3:00
Walker Elementary School 90 15 4.0 10.4 24 2:10
Ashland Middle School 90 15 5.3 11.1 29 2:15
John Muir Elementary School 90 15 0.3 10.1 2 1:50
Bellview Elementary School 90 15 6.9 21.1 20 2:05
Southern Oregon University 90 15 6.1 12.1 30 2:15
Maximum ETE: 3:00
Average ETE: 2:20Medical Facility Patient
Mobilization
(min)
Loading
Rate
(min per
person) People
Max Loading
Time (min)
Dist. To EMZ
Bdry (mi)
Speed
(mph)
Travel Time
to Safety
(min)
ETE
(hr:min)
Asante Ashland Community Hospital
Ambulatory 90 1 63 30 2.3 26.6 5 2:05
Wheelchair
Bound 90 5 31 75 2.3 29.8 5
2:50
Bedridden 90 30 31 60 2.3 22.4 6 2:40
Ashland Surgery Center
Ambulatory 90 1 13 13 2.0 20.7 6 1:50
Wheelchair
Bound 90 5 6 30 2.0 38.3 3
2:05
Bedridden 90 30 6 60 2.0 38.2 3 2:35
Linda Vista Nursing Home & Rehab Center
Ambulatory 90 1
50 30 2.1
38.3 3 2:05
Wheelchair
Bound 90 5
25 75 2.1
38.2 3 2:50
Bedridden 90 30
25 60 2.1
38.2 3 2:35
Maximum ETE: 2:50
Average ETE: 2:25
30
Bus Route
Number Bus Route
Number of
Buses
Mobilization
(min)
Route Length
(miles)
Speed
(mph)
Route
Travel Time
(min)
Pickup
Time
(min)
ETE
(hr:min)
1Servicing EMZ 1, EMZ 2 and EMZ 10 1 90 5.88 10.1 35 30 2:35
2Servicing EMZ 3 and EMZ 4 1 90 3.98 4.8 50 30 2:50
3Servicing EMZ 5 and EMZ 6 1 90 4.26 10.1 26 30 2:30
4Servicing EMZ 7, EMZ 8 and EMZ 9 1 90 3.82 5.2 45 30 2:45
Maximum ETE: 2:50
32
•Congestion persists in the EMZs for approximately 3
hours and 40 minutes.
•ETE are insensitive to changes in trip generation times up
to 3 hours and 40 minutes. Trip generation times longer
than that dictate ETE.
•Increases in shadow evacuee percentages have minimal
impacts to ETE (< 10 minutes).
Trip Generation Time
(Minutes)
Evacuation Time Estimates for All EMZs
90th Percentile 100th Percentile
3 Hours 3:05 3:40
4 Hours (Base)3:10 4:00
5 Hours 3:10 5:00
Percent Shadow
Evacuation
Evacuating Shadow
Beyond the Ridge
Line
Evacuation Time Estimate for All EMZs
90th Percentile 100th
Percentile
0 0 3:05 4:00
6 (Base)451 3:10 4:00
100 6,490 3:20 4:10
33
Case Evacuating
Resident Vehicles
Evacuation Time Estimates for All
Communities
90th Percentile 100th
Percentile
Base Case 13,666 3:10 4:00
One Vehicle per HH 9,560 2:40 4:00
34
Case
Evacuation Time Estimates for All EMZs
90th Percentile ETE 100
th Percentile ETE
Base Case 3:10 4:00
E Nevada St over Bear Creek 3:00 4:00
I‐5 Ramps near Nevada St 3:00 4:00
E Nevada St over Bear Creek and I‐5
Ramps 2:55 4:00
35
Case Evacuation Time Estimates for All EMZs
90th Percentile ETE 100
th Percentile
Base Case 3:10 4:00
Wildfire to the North
(Northbound Roadways Closed –Traffic forced Southbound)6:20 7:25
Wildfire to the South
(Southbound Roadways Closed –Traffic forced Northbound)5:35 6:35
Rebecca Cohen, PE, PTOE
(631) 524-5937
rcohen@kldcompanies.com
Brian Halpin
(631) 524-5925
bhalpin@kldcompanies.com
Rebecca Cohen, PE, PTOE
(631) 524-5937
rcohen@kldcompanies.com
Brian Halpin
(631) 524-5925
bhalpin@kldcompanies.com
36
March 2021 Draft Report, Rev. 0 KLD TR – 1217
City of Ashland
Evacuation Time Estimate Study
Work performed for the City of Ashland, by:
KLD Engineering, P.C.
1601 Veterans Memorial Highway, Suite 340
Islandia, NY 11749
e‐mail: kweinisch@kldcompanies.com
City of Ashland i KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
Table of Contents
1 INTRODUCTION .................................................................................................................................. 1‐1
1.1 Overview of the ETE Process ...................................................................................................... 1‐1
1.2 Location of the Study Area ......................................................................................................... 1‐3
1.3 Preliminary Activities ................................................................................................................. 1‐4
2 STUDY ESTIMATES AND ASSUMPTIONS ............................................................................................. 2‐1
2.1 Data Estimates ........................................................................................................................... 2‐1
2.2 Study Methodological Assumptions .......................................................................................... 2‐2
2.3 Study Assumptions ..................................................................................................................... 2‐2
3 DEMAND ESTIMATION ....................................................................................................................... 3‐1
3.1 Permanent Residents ................................................................................................................. 3‐2
3.1.1 Special Facilities ................................................................................................................. 3‐4
3.1.2 University Students ............................................................................................................ 3‐4
3.2 Shadow Population .................................................................................................................... 3‐5
3.3 Tourist Population ...................................................................................................................... 3‐5
3.4 Employees .................................................................................................................................. 3‐6
3.5 Medical Facilities ........................................................................................................................ 3‐7
3.6 Transit Dependent Population ................................................................................................... 3‐7
3.7 School Population Demand ........................................................................................................ 3‐9
3.8 External Traffic ......................................................................................................................... 3‐10
3.9 Background Traffic ................................................................................................................... 3‐11
3.10 Summary of Demand ............................................................................................................... 3‐11
4 ESTIMATION OF HIGHWAY CAPACITY ................................................................................................ 4‐1
4.1 Capacity Estimations on Approaches to Intersections .............................................................. 4‐2
4.2 Capacity Estimation along Sections of Highway ........................................................................ 4‐4
4.3 Application to the City of Ashland Study Area ........................................................................... 4‐6
4.3.1 Two‐Lane Roads ................................................................................................................. 4‐6
4.3.2 Multi‐Lane Highway ........................................................................................................... 4‐6
4.3.3 Freeways ............................................................................................................................ 4‐7
4.3.4 Intersections ...................................................................................................................... 4‐8
4.4 Simulation and Capacity Estimation .......................................................................................... 4‐8
4.5 Boundary Conditions .................................................................................................................. 4‐9
5 ESTIMATION OF TRIP GENERATION TIME .......................................................................................... 5‐1
5.1 Background ................................................................................................................................ 5‐1
5.2 Fundamental Considerations ..................................................................................................... 5‐2
5.3 Estimated Time Distributions of Activities Preceding Event 5 ................................................... 5‐3
5.4 Calculation of Trip Generation Time Distribution ...................................................................... 5‐4
5.4.1 Statistical Outliers .............................................................................................................. 5‐5
6 EVACUATION CASES ........................................................................................................................... 6‐1
7 GENERAL POPULATION EVACUATION TIME ESTIMATES (ETE) .......................................................... 7‐1
7.1 Voluntary Evacuation and Shadow Evacuation ......................................................................... 7‐1
7.2 Patterns of Traffic Congestion during Evacuation ..................................................................... 7‐2
7.3 Evacuation Rates ........................................................................................................................ 7‐3
7.4 Evacuation Time Estimate (ETE) Results .................................................................................... 7‐4
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Wildfire Egress Study Rev. 0
7.5 Guidance on Using ETE Tables ................................................................................................... 7‐5
8 ACCESS IMPAIRED NEIGHBORHOODS ................................................................................................ 8‐1
8.1 Data Sources .............................................................................................................................. 8‐1
8.2 Analysis ...................................................................................................................................... 8‐2
8.3 ETE Results, Safe Refuge Areas, and Evacuation Signage .......................................................... 8‐3
9 TRANSIT‐DEPENDENT AND SPECIAL FACILITY EVACUATION TIME ESTIMATES ................................. 9‐1
9.1 ETEs for Transit Dependent People ........................................................................................... 9‐2
10 TRAFFIC MANAGEMENT STRATEGY ............................................................................................. 10‐1
10.1 Assumptions ............................................................................................................................. 10‐2
10.2 Additional Considerations ........................................................................................................ 10‐3
11 EVACUATION ROUTES AND EVACUATION SIGNAGE ................................................................... 11‐1
11.1 Evacuation Routes .................................................................................................................... 11‐1
11.2 Evacuation Signage .................................................................................................................. 11‐2
A. GLOSSARY OF TRAFFIC ENGINEERING TERMS .................................................................................. A‐1
B. DYNAMIC TRAFFIC ASSIGNMENT AND DISTRIBUTION MODEL ......................................................... B‐1
C. DYNEV TRAFFIC SIMULATION MODEL ............................................................................................... C‐1
C.1 Methodology .............................................................................................................................. C‐2
C.1.1 The Fundamental Diagram ................................................................................................. C‐2
C.1.2 The Simulation Model ........................................................................................................ C‐2
C.1.3 Lane Assignment ................................................................................................................ C‐6
C.2 Implementation ......................................................................................................................... C‐6
C.2.1 Computational Procedure .................................................................................................. C‐6
C.2.2 Interfacing with Dynamic Traffic Assignment (DTRAD) ..................................................... C‐7
D. DETAILED DESCRIPTION OF STUDY PROCEDURE .............................................................................. D‐1
E. FACILITY DATA .................................................................................................................................... E‐1
F. DEMOGRAPHIC SURVEY ..................................................................................................................... F‐1
F.1 Introduction ............................................................................................................................... F‐1
F.2 Survey Instrument and Sampling Plan ....................................................................................... F‐1
F.3 Survey Results ............................................................................................................................ F‐2
F.3.1 Household Demographic Results ....................................................................................... F‐2
F.3.2 Evacuation Response ......................................................................................................... F‐3
F.3.3 Time Distribution Results ................................................................................................... F‐5
F.4 Conclusions ................................................................................................................................ F‐5
G EVACUATION REGIONS ..................................................................................................................... G‐1
H. EVACUATION ROADWAY NETWORK ................................................................................................. H‐1
J. EVACUATION SENSITIVITY STUDIES ................................................................................................... J‐1
J.1 Effect of Changes in Trip Generation Times .............................................................................. J‐1
J.2 Effect of Changes in the Number of People in the Shadow Region Who Relocate ................... J‐1
J.3 Effect of Reducing the Evacuation Demand – One Vehicle per Household ............................... J‐2
J.4 Effect of Direction of Wildfire Approach ................................................................................... J‐2
J.4.1 A Wildfire to the North wherein Traffic is Forced Southbound ......................................... J‐2
J.4.2 A Wildfire to the South wherein Traffic is Forced Northbound ......................................... J‐3
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Wildfire Egress Study Rev. 0
J.4.3 Patterns of Traffic Congestion due to Wildfire Approach.................................................. J‐3
J.5 Additional “What‐if” Scenarios .................................................................................................. J‐4
Note: Appendix I intentionally skipped
City of Ashland iv KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
List of Figures
Figure 1‐1. Study Area Location ................................................................................................................ 1‐8
Figure 1‐2. Study Area Link‐Node Analysis Network ................................................................................ 1‐9
Figure 3‐1. EMZ Boundaries .................................................................................................................... 3‐19
Figure 3‐2. Census Boundaries within the Study Area ............................................................................ 3‐20
Figure 4‐1. Fundamental Diagrams ......................................................................................................... 4‐10
Figure 5‐1. Events and Activities Preceding the Evacuation Trip ............................................................ 5‐13
Figure 5‐2. Evacuation Mobilization Activities ........................................................................................ 5‐14
Figure 5‐3. Comparison of Data Distribution and Normal Distribution .................................................. 5‐15
Figure 5‐4. Comparison of Trip Generation Distributions ....................................................................... 5‐16
Figure 6‐1. EMZ Boundaries ...................................................................................................................... 6‐7
Figure 7‐1. Study Area Shadow Region ..................................................................................................... 7‐9
Figure 7‐2. Congestion Patterns at 30 Minutes after the Advisory to Evacuate .................................... 7‐10
Figure 7‐3. Congestion Patterns at 1 Hour after the Advisory to Evacuate ............................................ 7‐11
Figure 7‐4. Congestion Patterns at 2 Hours after the Advisory to Evacuate .......................................... 7‐12
Figure 7‐5. Congestion Patterns at 3 Hours after the Advisory to Evacuate .......................................... 7‐13
Figure 7‐6. Congestion Patterns at 3 Hours and 30 Minutes after the Advisory to Evacuate ................ 7‐14
Figure 7‐7. Congestion Patterns at 4 Hours after the Advisory to Evacuate .......................................... 7‐15
Figure 7‐8. Evacuation Time Estimates ‐ Scenario 1 for Region R18 ...................................................... 7‐16
Figure 7‐9. Evacuation Time Estimates ‐ Scenario 2 for Region R18 ...................................................... 7‐17
Figure 7‐10. Evacuation Time Estimates ‐ Scenario 3 for Region R18 .................................................... 7‐18
Figure 7‐11. Evacuation Time Estimates ‐ Scenario 4 for Region R18 .................................................... 7‐19
Figure 7‐12. Evacuation Time Estimates ‐ Scenario 5 for Region R18 .................................................... 7‐20
Figure 7‐13. Evacuation Time Estimates ‐ Scenario 6 for Region R18 .................................................... 7‐21
Figure 8‐1. Access Impaired Neighborhoods with the City Limits ............................................................ 8‐4
Figure 8‐2. Aggregated Tax Lots ................................................................................................................ 8‐5
Figure 8‐3. Neighborhoods without Low Density Resident Areas ............................................................ 8‐6
Figure 8‐4. Neighborhoods without Low Density Resident Areas and Moderate to Very High Wildfire
Risk to People and Property ....................................................................................................................... 8‐7
Figure 8‐5. Resident Address Points ......................................................................................................... 8‐8
Figure 8‐6. Access Impaired Neighborhoods outside of the City Limits ................................................... 8‐9
Figure 8‐7. Combined Access Impaired Neighborhoods ......................................................................... 8‐10
Figure 9‐1. Chronology of Transit Evacuation Operations ...................................................................... 9‐10
Figure 11‐1. Evacuation Route Map ........................................................................................................ 11‐6
Figure 11‐2. Transit‐Dependent Bus Routes Servicing the EMZ ............................................................. 11‐7
Figure 11‐3. Evacuation Route Sign Example .......................................................................................... 11‐8
Figure B‐1. Flow Diagram of Simulation‐DTRAD Interface ........................................................................ B‐4
Figure C‐1. Representative Analysis Network ......................................................................................... C‐12
Figure C‐2. Fundamental Diagrams ......................................................................................................... C‐13
Figure C‐3. A UNIT Problem Configuration with t1 > 0 ............................................................................ C‐13
Figure C‐4. Flow of Simulation Processing (See Glossary: Table C‐3) .................................................... C‐14
Figure D‐1. Flow Diagram of Activities ..................................................................................................... D‐5
Figure E‐1. Schools and Preschools/Daycares within the EMZ ................................................................. E‐5
Figure E‐2. Medical Facilities within the EMZ ........................................................................................... E‐6
Figure E‐3. Major Employers within the EMZ ........................................................................................... E‐7
Figure E‐4. Recreational Facilities and Lodging Facilities within the EMZ ................................................ E‐8
City of Ashland v KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
Figure F‐1. Household Size in the Study Area ........................................................................................... F‐6
Figure F‐2. Vehicle Availability .................................................................................................................. F‐6
Figure F‐3. Vehicle Availability ‐ 1 to 4 Person Households ...................................................................... F‐7
Figure F‐4. Vehicle Availability – 5 to 7+ Person Households ................................................................... F‐7
Figure F‐5. Electric Vehicle Ownership ..................................................................................................... F‐8
Figure F‐6. Functional or Transportation Needs ....................................................................................... F‐8
Figure F‐7. Commuters in Households in the Study Area ......................................................................... F‐9
Figure F‐8. Modes of Travel in the Study Area .......................................................................................... F‐9
Figure G‐1. Region R01 ............................................................................................................................. G‐3
Figure G‐2. Region R02 ............................................................................................................................. G‐4
Figure G‐3. Region R03 ............................................................................................................................. G‐5
Figure G‐4. Region R04 ............................................................................................................................. G‐6
Figure G‐5. Region R05 ............................................................................................................................. G‐7
Figure G‐6. Region R06 ............................................................................................................................. G‐8
Figure G‐7. Region R07 ............................................................................................................................. G‐9
Figure G‐8. Region R08 ........................................................................................................................... G‐10
Figure G‐9. Region R09 ........................................................................................................................... G‐11
Figure G‐10. Region R10 ......................................................................................................................... G‐12
Figure G‐11. Region R11 ......................................................................................................................... G‐13
Figure G‐12. Region R12 ......................................................................................................................... G‐14
Figure G‐13. Region R13 ......................................................................................................................... G‐15
Figure G‐14. Region R14 ......................................................................................................................... G‐16
Figure G‐15. Region R15 ......................................................................................................................... G‐17
Figure G‐16. Region R16 ......................................................................................................................... G‐18
Figure G‐17. Region R17 ......................................................................................................................... G‐19
Figure G‐18. Region R18 ......................................................................................................................... G‐20
Figure H‐1. Evacuation Time Estimate Study Link‐Node Analysis Network ........................................... H‐21
Figure H‐2. Link‐Node Analysis Network – Grid 1 .................................................................................. H‐22
Figure H‐3. Link‐Node Analysis Network – Grid 2 ................................................................................... H‐23
Figure H‐4. Link‐Node Analysis Network – Grid 3 ................................................................................... H‐24
Figure H‐5. Link‐Node Analysis Network – Grid 4 ................................................................................... H‐25
Figure H‐6. Link‐Node Analysis Network – Grid 5 ................................................................................... H‐26
Figure H‐7. Link‐Node Analysis Network – Grid 6 ................................................................................... H‐27
Figure H‐8. Link‐Node Analysis Network – Grid 7 ................................................................................... H‐28
Figure H‐9. Link‐Node Analysis Network – Grid 8 ................................................................................... H‐29
Figure H‐10. Link‐Node Analysis Network – Grid 9 ................................................................................. H‐30
Figure H‐11. Link‐Node Analysis Network – Grid 10 ............................................................................... H‐31
Figure H‐12. Link‐Node Analysis Network – Grid 11 ............................................................................... H‐32
Figure H‐13. Link‐Node Analysis Network – Grid 12 ............................................................................... H‐33
Figure H‐14. Link‐Node Analysis Network – Grid 13 ............................................................................... H‐34
Figure H‐15. Link‐Node Analysis Network – Grid 14 ............................................................................... H‐35
Figure J‐1. Wildfire Approach Congestion Pattern Comparison at 1 Hour after the Advisory
to Evacuate ................................................................................................................................................ J‐7
Figure J‐2. Wildfire Approach Congestion Pattern Comparison at 2 Hours after the Advisory
to Evacuate ................................................................................................................................................ J‐8
Figure J‐3. Wildfire Approach Congestion Pattern Comparison at 3 Hours after the Advisory
to Evacuate ................................................................................................................................................ J‐9
City of Ashland vi KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
Figure J‐4. Wildfire Approach Congestion Pattern Comparison at 4 Hours after the Advisory
to Evacuate .............................................................................................................................................. J‐10
Figure J‐5. Wildfire Approach Congestion Pattern Comparison at 5 Hours after the Advisory
to Evacuate .............................................................................................................................................. J‐11
Figure J‐6. Wildfire Approach Congestion Pattern Comparison at 6 Hours after the Advisory
to Evacuate .............................................................................................................................................. J‐12
Figure J‐7. Wildfire Approach Congestion Pattern Comparison at 7 Hours after the Advisory
to Evacuate .............................................................................................................................................. J‐13
Figure J‐8. Wildfire Approach Congestion Pattern Comparison at 7 Hours and 20 Minutes after the
Advisory to Evacuate ................................................................................................................................ J‐14
Figure J‐9. E Nevada St Bridge and I‐5 On Ramps ................................................................................... J‐15
City of Ashland vii KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
List of Tables
Table 1‐1. Stakeholder Interaction ........................................................................................................... 1‐7
Table 1‐2. Highway Characteristics ........................................................................................................... 1‐7
Table 2‐1. Evacuation Scenario Definitions ............................................................................................... 2‐5
Table 3‐1. County Population Change and Annual Growth Rate from April 1, 2010 to July 1, 2019 ..... 3‐12
Table 3‐2. Municipality Population Change and Annual Growth Rate from April 1, 2010 to
July 1, 2019 .............................................................................................................................................. 3‐12
Table 3‐3. EMZ Permanent Resident Population .................................................................................... 3‐12
Table 3‐4. Permanent Resident Population and Vehicles by EMZ .......................................................... 3‐13
Table 3‐5. Summary of Tourists and Tourist Vehicles ............................................................................. 3‐13
Table 3‐6. Summary of Employees and Employee Vehicles Commuting into the EMZ .......................... 3‐14
Table 3‐7. Medical Facilities Transit Demand Estimates ......................................................................... 3‐15
Table 3‐8. Transit‐Dependent Population Estimates .............................................................................. 3‐15
Table 3‐9. School and Preschool/Daycare Population Demand Estimates ............................................. 3‐16
Table 3‐10. Study Area External Traffic Demand .................................................................................... 3‐16
Table 3‐11. Summary of Population Demand ......................................................................................... 3‐17
Table 3‐12. Summary of Vehicle Demand ............................................................................................... 3‐18
Table 5‐1. Event Sequence for Evacuation Activities ................................................................................ 5‐8
Table 5‐2. Time Distribution for Notifying the Public ............................................................................... 5‐8
Table 5‐3. Time Distribution for Employees to Prepare to Leave Work/College ...................................... 5‐9
Table 5‐4. Time Distribution for Commuters to Travel Home ................................................................ 5‐10
Table 5‐5. Time Distribution for Population to Prepare to Evacuate ..................................................... 5‐11
Table 5‐6. Mapping Distributions to Events ............................................................................................ 5‐11
Table 5‐7. Description of the Distributions ............................................................................................. 5‐12
Table 5‐8. Trip Generation Histograms for the EMZ Population ............................................................ 5‐12
Table 6‐1. Description of Evacuation Regions ........................................................................................... 6‐3
Table 6‐2. Evacuation Scenario Definitions ............................................................................................... 6‐4
Table 6‐3. Percent of Population Groups Evacuating for Various Scenarios ............................................ 6‐5
Table 6‐4. Vehicle Estimates by Scenario .................................................................................................. 6‐6
Table 7‐1. Time to Clear the Indicated Area of 90 Percent of the Affected Population ........................... 7‐7
Table 7‐2. Time to Clear the Indicated Area of 100 Percent of the Affected Population ......................... 7‐8
Table 9‐1. Summary of Transportation Needs and Resources ................................................................. 9‐6
Table 9‐2. School Evacuation Time Estimates ........................................................................................... 9‐7
Table 11‐1. Summary of Transit‐Dependent Bus Routes ......................................................................... 11‐3
Table 11‐2. Bus Route Description ........................................................................................................... 11‐4
Table A‐1. Glossary of Traffic Engineering Terms .................................................................................... A‐1
Table C‐1. Selected Measures of Effectiveness Output by DYNEV II ........................................................ C‐8
Table C‐2. Input Requirements for the DYNEV II Model ........................................................................... C‐9
Table C‐3. Glossary .................................................................................................................................. C‐10
Table E‐1. Schools and Preschools/Daycares within the EMZ .................................................................. E‐2
Table E‐2. Medical Facilities within the EMZ ............................................................................................ E‐3
Table E‐3. Major Employers within the EMZ............................................................................................. E‐3
Table E‐4. Recreational Facilities and Lodging Facilities within the EMZ.................................................. E‐4
Table G‐1. Percent of EMZ Population Evacuating for Each Region ........................................................ G‐2
Table H‐1. Evacuation Roadway Network Characteristics ....................................................................... H‐2
Table H‐2. Nodes in the Link‐Node Analysis Network which are Controlled ......................................... H‐18
City of Ashland viii KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
Table J‐1. Evacuation Time Estimates for Trip Generation Sensitivity Study ............................................ J‐5
Table J‐2. Evacuation Time Estimates for Shadow Sensitivity Study ........................................................ J‐5
Table J‐3. Evacuation Time Estimates for Reduction in Demand .............................................................. J‐6
Table J‐4. Evacuation Time Estimates for Direction of Wildfire Approach ............................................... J‐6
Table J‐5. Evacuation Time Estimates for Additional “What‐if” Scenarios ............................................. J‐16
City of Ashland ES‐1 KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
EXECUTIVE SUMMARY
Wildfires, and the impacts thereof, are a critical issue facing the world. Increased temperatures, drought,
unusually low humidity, and increased winds contribute to the increase in frequency and severity of
wildfires. Of the numerous concerns surrounding wildfire emergencies, one of the most critical is the
availability of transportation services and facilities. Under normal circumstances, the transportation
system provides capacity for evacuation (of both those who can evacuate independently as well as those
who need transportation assistance) and allows for emergency responders to enter an area at risk. During
a wildfire, however, the transportation system can become inadequate due to unsafe roadway conditions,
abandoned vehicles blocking the roadway, and/or congestion. As a result, the risk to public health and
the environment – and the potential for loss of life – increases.
The City of Ashland is a small mountain town of about 6 square miles that has a very unique linear design
as it rests in three mountain ranges – the Cascades to the east, the coastal range to the west, and the
Siskiyous to the south. The City is located 15 miles north of the California border and is home to over
20,000 residents. Due to its topography, the City has limited ingress/egress routes. Highway 99, Highway
66, and Interstate‐5 are the major roadways servicing the City.
As such, there is an urgent need to identify areas and populations most vulnerable to fire and to develop
a plan for evacuations in the City of Ashland. This study analyzed traffic conditions and evacuation times
for a variety of evacuation scenarios of the City of Ashland. Alternative emergency management strategies
that could be used in response to an evacuation of the City were also examined. This study, and the results
contained within this report, will further inform the City’s emergency planning and protective action
decision making.
A traffic/evacuation simulation model (Dynamic Evacuation Simulation Model, or DYNEV‐II) is used to
compute evacuation time estimates (ETE) using the procedure shown in Figure ES‐1. The supply and
demand are input to DYNEV‐II. The supply input to DYNEV‐II is in the form of a link‐node analysis network
– a computerized replica of the roadway system within the study area (see Appendix H). The link‐node
analysis network is calibrated to include roadway characteristics such as free speed (speed that drivers
are comfortable traveling at in the lack of traffic congestion), number of lanes, type of traffic control
(signal, stop sign, manned), etc. that were collected during a field survey in July 2020. The demand input
to DYNEV‐II includes people that could be living, working or recreating in the area who may need to be
evacuated during an emergency. Resident population was obtained from the 2010 US Census and was
projected to the year 2020. Employee and tourist data were obtained from the the US Census Longitudinal
Employer‐Household Dynamics and from local stakeholders, supplemented by internet searches. Special
facilities that may need to be evacuated include schools, preschools, and medical facilities. Lastly, external
traffic are vehicles that have their origin and destination outside of Ashland but travel through the city on
their route thereby potentially delaying evacuating vehicles from within the city.
The two main outputs of the DYNEV‐II model are ETE for general population (evacuees with personal
vehicles) and route‐specific evacuation speeds, which are used to compute the ETE for special facilities
(schools and medical facilities) and the transit‐dependent population. These times are critical for
developing an effective plan to protect the health and safety of the public.
City of Ashland ES‐2 KLD Engineering, P.C.
Wildfire Egress Study Rev. 0
City officials have divided the City of Ashland into 10 Emergency Management Zones (EMZs). The
boundaries of the EMZs follow political or geographical boundaries, which helps the City communicate
evacuation orders to the public. Given the large wilderness areas surrounding the City of Ashland, it is
highly unlikely that a wildfire would only impact Ashland. Rather, neighboring communities which are also
along the ridgelines of these wildnerness areas are likely to evacuate at the same time. The vehicles
evacuating from these neighboring communities could delay egress from Ashland; this phenomenon is
referred to as a “shadow evacuation.” Figure 3‐2 shows the EMZs that comprise the City of Ashland, as
well as the shadow evacuation region surrounding the City and encompassing the neighbouring city of
Talent.
The general population ETE are presented in Tables 7‐1 and 7‐2. These data are the times needed to clear
the indicated regions (individual EMZs or groupings of EMZs) of 90 and 100 percent of the population
occupying these regions, respectively. For definitions of scenarios (demand changes due to temporal
variations) and regions (area to be evacuated varies by wildfire situation), see Section 6 and Appendix G,
respectively. The 100th percentile ETE is defined as the time when the last evacuation vehicle crosses the
boundary of the regions shown in Appendix G. These computed ETE include consideration of mobilization
time (how long it takes people to prepare to evacuate prior to actually getting in their vehicle and driving
out of the area) and of estimated voluntary evacuations from areas outside of the area given the
evacuation order.
Critical findings of the study include:
The 100th percentile ETE for the evacuation of individual EMZs are entirely dictated by the time
needed to mobilize rather than by traffic congestion. All traffic congestion clears prior to the
completion of mobilization for all regions. As such, it is recommended that the 90th percentile is
used when making protective action decisions. (See Section 7.4). It is highly unlikely that an
individual EMZ would be evacuated for a wildfire. It is more likely that an individual EMZ would be
evacuated for a smaller event such as a gas leak or a HAZMAT spill. Thus, the ETE for individual
EMZs presented in this study can be used for hazards other than wildfire.
The 90th percentile ETE ranges between 1 hour and 20 minutes and 3 hours and 10 minutes.
Evacuations involving multiple EMZs have longer ETE than evacuations of individual EMZs. See
Table 7‐1.
Several access impaired neighborhoods have been identified and are shown in Table 8‐7. These
areas have high wildfire risk, high population densities, and limited means of egress. These
neighborhoods would require early notification during an approaching wildfire as they may have
difficulty evacuating if a fire were present.
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Transportation resources available were provided by city emergency management
representatives. Table 9‐1 summarizes the information received. Also included in the table are the
number of buses needed to evacuate schools, medical facilities and transit‐dependent population.
These numbers indicate there are not sufficient resources available to evacuate everyone in a
single wave. (See Section 9). There are two ways to handle this shortfall of transportation
resources: (1) the same vehicles are used multiple times; passengers are picked up and dropped
off outside of the area at risk. The vehicle then returns to the City to pick up additional passengers.
(2) Memorandums of Understanding (MOUs) or Mutual Aid Agreements are established with
neighboring cities or with the State to provide additional transportation resources to assist with
evacuation.
One traffic control point (TCP) was recommended as a result of the findings of this study: at the
intersection of OR‐99 and Crowson Rd This ETE was modeled explicitly in the ETE simulations (See
Section 10).
Congestion within the ETE exists within the EMZs for just over 3 hours and 30 minutes. As such, if
the time to mobilize is less than 3 hours and 40 minutes, congestion dictates the 100th percentile
ETE. If the time to mobilize is longer than 3 hours and 40 minutes, the 100th percentile is dictated
by the trip generation time. See Section J.1.
The Shadow Region was defined as the area beyond the EMZ including the City of Talent to the
north, Emigrant Lake to the south and the surrounding ridgelines. All of these areas are sparsely
populated areas. Therefore, changes in the percentage of people that decide to voluntarily
evacuate beyond the city limits will have little to no impact (at most 10 minutes) on an evacuation
of the City of Ashland.
If the evacuation demand could be reduced – by limiting to one evacuating vehicle per household,
for example – the 90th percentile ETE decreases by 30 minutes.
The entire City takes 4 hours, on average, if the city can evacuate in either direction.
o For an evacuation case wherein a wildfire is in the north forcing evacuees southbound, the
90th percentile ETE increases by 3 hours and 25 minutes at most. See Section J.4.
o For an evacuation case wherein a wildfire is in the south forcing evacuees northbound, the
90th percentile ETE increases by 2 hours and 35 minutes at most. See Section J.4.
Two roadway improvement projects were tested to determine their impact on ETE:
o The addition of a bridge to reconnect E Nevada St over Bear Creek decreases the 90th
percentile ETE by 10 minutes. See Section J.5.
o The addition of ramps to I‐5 along N Mountain Ave decreases the 90th percentile ETE by 10
minutes. See Section J.5.
o If both the bridge and I‐5 Freeway Ramps were implemented, the 90th percentile ETE
decreases by 15 minutes. See Section J.5.
The City of Ashland should consider revising their emergency plans to incorporate the lessons learned
from this study. Once revised, the new procedures developed should be practiced through exercises and
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drills. Lessons learned from these exercises and drills should then be used to improve the emergency
plans further. Emergency planning is an iterative process.
City residents and visitors should be informed, through public outreach, of the emergency plan contents,
including how they will be notified, which evacuation routes to use, what to do if they need transportation
assistance, how long it might take to evacuate, and what would happen if critical evacuation routes are
unavailable. Citizen participation in evacuation drills is recommended. Education is key in protecting
public health and safety.
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SUPPLY
•Evacuation Routes
•Number of Lanes
•Roadway Capacity
•Traffic Control Devices
•Traffic Control Tactics
ETE for General Population
(Residents, Tourists,
Employees)
DEMAND
•Permanent Residents
•Tourists
•Employees
•Special Events
•External Traffic
•Special Facilities
DYNEV-II
DYNAMIC TRAFFIC SIMULATION MODEL
Avg. Speed of
Evacuating Vehicles
ETE for Special
Facilities
Figure ES‐1. ETE Methodology
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Table 7‐1. Time to Clear the Indicated Area of 90 Percent of the Affected Population
Summer Fall
Midweek Weekend Midweek
Weekend Midweek Weekend Midweek
Weekend
Scenario: (1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 – EMZ 1 1:55 1:45 1:40 1:55 1:45 1:40
R02 – EMZ 2 1:55 1:50 1:45 1:55 1:50 1:45
R03 – EMZ 3 1:40 1:50 1:45 1:35 1:50 1:45
R04 – EMZ 4 2:00 1:55 1:55 2:00 1:55 1:55
R05 – EMZ 5 1:30 1:25 1:20 1:30 1:30 1:20
R06 – EMZ 6 1:50 1:50 1:50 1:45 1:50 1:50
R07 – EMZ 7 1:50 1:50 1:50 1:50 1:50 1:50
R08 – EMZ 8 1:50 1:55 1:50 1:50 1:55 1:50
R09 – EMZ 9 1:55 1:50 1:50 1:55 1:50 1:50
R10 – EMZ 10 1:55 1:55 1:55 1:55 1:55 1:55
R11 ‐ Western Ashland 1:50 1:50 1:45 1:35 1:50 1:45
R12 ‐ Eastern Ashland 1:50 1:50 1:50 1:55 1:50 1:50
R13 ‐ Northern Ashland 2:00 1:55 1:50 2:00 1:55 1:50
R14 ‐ Central Ashland 1:45 1:50 1:45 2:30 1:50 1:45
R15 ‐ Southern Ashland 2:05 1:55 1:55 2:45 1:55 1:55
R16 ‐ Northern and Central
Ashland 1:55 1:50 1:50 2:30 1:50 1:50
R17 ‐ Southern and Central
Ashland 1:55 1:50 1:50 1:50 1:50 1:50
R18 ‐ All EMZs 2:35 2:25 2:15 3:10 2:25 2:20
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Table 7‐2. Time to Clear the Indicated Area of 100 Percent of the Affected Population
Summer Fall
Midweek Weekend Midweek
Weekend Midweek Weekend Midweek
Weekend
Scenario: (1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 – EMZ 1 4:00 4:00 4:00 4:00 4:00 4:00
R02 – EMZ 2 4:00 4:00 4:00 4:00 4:00 4:00
R03 – EMZ 3 4:00 4:00 4:00 4:00 4:00 4:00
R04 – EMZ 4 4:00 4:00 4:00 4:00 4:00 4:00
R05 – EMZ 5 4:00 4:00 4:00 4:00 4:00 4:00
R06 – EMZ 6 4:00 4:00 4:00 4:00 4:00 4:00
R07 – EMZ 7 4:00 4:00 4:00 4:00 4:00 4:00
R08 – EMZ 8 4:00 4:00 4:00 4:00 4:00 4:00
R09 – EMZ 9 4:00 4:00 4:00 4:00 4:00 4:00
R10 – EMZ 10 4:00 4:00 4:00 4:00 4:00 4:00
R11 ‐ Western Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R12 ‐ Eastern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R13 ‐ Northern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R14 ‐ Central Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R15 ‐ Southern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R16 ‐ Northern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R17 ‐ Southern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R18 ‐ All EMZs 4:00 4:00 4:00 4:00 4:00 4:00
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Figure 8‐7. Combined Access Impaired Neighborhoods
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Table 9‐1. Summary of Transportation Needs
Transportation
Resource Buses
Wheelchair
Buses Ambulances
Resources Available
Ashland School District 19 0 0
Ashland Fire & Rescue 0 0 2
Jackson County Fire District 0 0 2‐4*
TOTAL: 19 0 2
Resources Needed
Medical Facilities (Table 3‐7): 6 6 32
Schools (Table 3‐9): 75 0 0
Transit‐Dependent Population (Table 11‐1): 4 0 0
TOTAL TRANSPORTATION NEEDS: 85 6 32
*Note: 2‐4 Ambulances can be made available from Jackson County Fire Department in the case that more are needed.
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1 INTRODUCTION
This section provides an introduction of the study and an overview of the process used to
compute the evacuation time estimates (ETE) for the City of Ashland, including preliminary
activities of the project.
This study analyzed traffic conditions and evacuation times for a variety of evacuation scenarios
of the City of Ashland. Alternative emergency management strategies that could be used in
response to an evacuation of the City of Ashland were also examined. This study, and the
results contained within this report, will further inform the City of Ashland’s emergency
planning and protective action decision making.
In the performance of this effort, guidance is provided by documents published by Federal and
State Governmental agencies. The nuclear industry is highly regulated and offers a number of
resources for developing evacuation studies. Very few such documents exist for wildfire
hazards. While the hazard is different, much of the concepts of evacuation (warning time,
smaller/isolated communities, lower density roadway networks, etc.) are appliable. As such,
most of the references used in this study have been published by the US Nuclear Regulatory
Commission (NRC). Most important of these are:
• Title 10, Code of Federal Regulations, Appendix E to Part 50 (10CFR50), Emergency
Planning and Preparedness for Production and Utilization Facilities, NRC, 2011.
• Criteria for Development of Evacuation Time Estimate Studies, NUREG/CR‐7002,
November 2011.
• Criteria for Preparation and Evaluation of Radiological Emergency Response Plans
and Preparedness in Support of Nuclear Power Plants, NUREG 0654/FEMA REP 1,
Rev. 1, November 1980.
• Analysis of Techniques for Estimating Evacuation Times for Emergency Planning
Zones, NUREG/CR 1745, November 1980.
The work effort reported herein was supported and guided by local stakeholders who
contributed suggestions, critiques, and the local knowledge base required. Table 1‐1 presents a
summary of stakeholders and interactions.
1.1 Overview of the ETE Process
The following outline presents a brief description of the work effort in chronological sequence:
1. Information Gathering:
a. Defined the scope of work in discussions with representatives from the City
of Ashland.
b. Attended meetings with local stakeholders to define methodology.
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c. Conducted a detailed field survey of the highway system and of area traffic
conditions within the Emergency Management Zones (EMZs) and Shadow
Region1.
d. Obtained demographic data from the 2010 Census. Projected the 2010
Census data to the year 2020 (see Section 3.1).
e. Estimated the number of non‐EMZ employees using data obtained from the
US Census Longitudinal Employer‐Household Dynamics from the OnTheMap
Census analysis tool (see Section 3.4).
f. Conducted a random sample demographic survey of EMZ residents.
g. Obtained data (to the extent available) to update the database of schools,
colleges, medical facilities, tourist attractions, recreational facilities, and
transportation resources available. Majority of this data was provided by the
City supplemented with internet searches.
2. Estimated distribution of trip generation times representing the time required by
various population groups (permanent residents, employees, and tourists) to
prepare for the evacuation trip (mobilize) and updated where necessary. These
estimates were based upon the demographic survey results and notification time
calculation (see Section 5 and Appendix F).
3. Defined Evacuation Scenarios. These scenarios reflect the variation in demand
associated with different seasons, day of week, and time of day. The scenarios
selected were bound by the normal wildfire season.
4. Created Evacuation Regions. “Regions” are individual or groups of EMZs for which
ETE are calculated. The configurations of these Regions reflect evacuation of each
EMZ and a combination of EMZs (see Appendix G).
5. Estimated demand for transit services for persons at special facilities and for transit‐
dependent persons.
6. Identified and mapped access impaired neighborhoods in and around the City of
Ashland wherein there are medium to high population densities, high wildfire risk,
and limited means of egress.
7. Prepared the input streams for the DYNEV II system which computes ETE (see
Appendices B and C).
a. Estimated the evacuation traffic demand, based on the available information
derived from Census data, from data provided by local agencies, and from
the demographic survey.
b. Created the link‐node representation of the evacuation network, which was
1 An evacuation in the shadow region occurs when residents voluntarily evacuate from areas beyond the area officially given the
evacuation order. This phenomenon can cause unwanted congestion and increase clearance times for people in the areas of actual
risk.
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used as the basis for the computer analysis that calculates the ETE.
c. Applied the procedures specified in the 2016 Highway Capacity Manual
(HCM2) to the data acquired during the field survey, to estimate the capacity
of all highway segments comprising the evacuation routes.
d. Calculated the evacuating traffic demand for each Region and for each
Scenario.
e. Specified selected candidate destinations for each “origin” (location of each
“source” where evacuation trips are generated over the mobilization time) to
support evacuation travel consistent with outbound movement relative to
the location of the wildfire.
8. Executed the DYNEV II model to determine optimal evacuation routing and compute
ETE for all residents, tourists and employees (“general population”) with access to
private vehicles. Generated a complete set of ETE for all specified Regions and
Scenarios.
9. Identified Traffic Control Points (TCP) and Access Control Points (ACP) within the
study area. See Section 10.
10. Calculated the ETE for all transit activities including those for special facilities
(schools and medical facilities) and for the transit‐dependent population.
11. Documented ETE results.
12. Considered two additional cases to represent bounding conditions for possible
wildfire scenarios:
a. A case wherein the fire is originating from the north and traveling southbound
toward the City of Ashland. In this case, all evacuees are forced to evacuate to
the south.
b. A case wherein the fire is originating from the south and traveling northbound
toward the City of Ashland. In this case, all evacuees are forced to evacuate to
the north.
13. Tested what‐if scenarios to evaluate alternative management strategies that could
be used in response to wildfire situations.
1.2 Location of the Study Area
The City of Ashland is located in Jackson County, Oregon, approximately 14 miles southeast of
Medford, OR. Figure 1‐1 displays the area surrounding the EMZs. This map identifies the major
roadways and Shadow Region as well.
2 Highway Capacity Manual (HCM 2016), Transportation Research Board, National Research Council, 2016.
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1.3 Preliminary Activities
These activities are described below.
Field Surveys of the Highway Network
KLD personnel drove the entire highway system within the EMZ and the Shadow Region. The
Shadow Region considered for the City of Ashland ETE was defined as the area beyond the EMZ
including the City of Talent. The Shadow Region is bounded by the northern city limit of Talent
to the north, the ridge line and the United States Forest Service (USFS) border to the west, a
horizontal line connecting the USFS border to Emigrant Lake to the south, and the ridge line to
the east, shown in Figure 1‐1. The characteristics of each section of highway were recorded.
These characteristics are shown in Table 1‐2.
Video and audio recording equipment were used to capture a permanent record of the highway
infrastructure. No attempt was made to meticulously measure such attributes as lane width
and shoulder width; estimates of these measures based on visual observation and recorded
images were considered appropriate for the purpose of estimating the capacity of highway
sections. For example, Exhibit 15‐7 in the HCM indicates that a reduction in lane width from 12
feet (the “base” value) to 10 feet can reduce free flow speed (FFS) by 1.1 mph – not a material
difference – for two‐lane highways. Exhibit 15‐46 in the HCM shows little sensitivity for the
estimates of Service Volumes at Level of Service (LOS) E (near capacity), with respect to FFS, for
two‐lane highways.
The data from the audio and video recordings were used to create detailed geographic
information systems (GIS) shapefiles and databases of the roadway characteristics and of the
traffic control devices observed during the road survey; this information was referenced while
preparing the input stream for the DYNEV II System.
As documented on page 15‐6 of the HCM 2016, the capacity of a two‐lane highway is 1700
passenger cars per hour in one direction. For freeway sections, a value of 2250 vehicles per
hour per lane is assigned, as per Exhibit 12‐37 of the HCM 2016. The road survey has identified
several segments which are characterized by adverse geometrics (steep hills and tight curves
with no shoulders) on two‐lane highways which are reflected in reduced values for both
capacity and speed. These estimates are consistent with the service volumes for LOS E
presented in HCM Exhibit 15‐46. These links may be identified by reviewing Appendix H. Link
capacity is an input to DYNEV II which computes the ETE. Further discussion of roadway
capacity is provided in Section 4 of this report.
Traffic signals are either pre‐timed (signal timings are fixed over time and do not change with
the traffic volume on competing approaches) or are actuated (signal timings vary over time
based on the changing traffic volumes on competing approaches). Actuated signals require
detectors to provide the traffic data used by the signal controller to adjust the signal timings.
These detectors are typically magnetic loops in the roadway, or video cameras mounted on the
signal masts and pointed toward the intersection approaches. If detectors were observed on
the approaches to a signalized intersection during the road survey, detailed signal timings were
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not collected as the timings vary with traffic volume. Traffic Control Points at locations which
have control devices are represented as actuated signals in the DYNEV II system.
If no detectors were observed, the signal control at the intersection was considered pre‐timed,
and detailed signal timings were gathered for several signal cycles. These signal timings were
input to the DYNEV II system used to compute ETE.
Figure 1‐2 presents the link‐node analysis network that was constructed to model the
evacuation roadway network in the EMZ and Shadow Region. The directional arrows on the
links and the node numbers have been removed from Figure 1‐2 to clarify the figure. The
detailed figures provided in Appendix H depict the analysis network with directional arrows
shown and node numbers provided. The observations made during the field survey along with
aerial imagery were used to calibrate the analysis network.
Demographic Survey
A demographic survey was performed to gather information needed for the evacuation study.
Appendix F presents the survey instrument, the procedures used, and tabulations of data
compiled from the survey returns.
This data was utilized to develop estimates of vehicle occupancy to estimate the number of
evacuating vehicles during an evacuation and to estimate elements of the mobilization process.
This database was also referenced to estimate the number of transit‐dependent people.
Computing the Evacuation Time Estimates
The overall study procedure is outlined in Appendix D. Demographic data was obtained from
several sources, as detailed later in this report. These data were analyzed and converted into
vehicle demand data. The vehicle demand was loaded onto appropriate “source” links of the
analysis network using GIS mapping software. The DYNEV II system was then used to compute
ETE for all Regions and Scenarios.
Analytical Tools
The DYNEV II System5 that was employed for this study is comprised of several integrated
computer models. One of these is the DYNEV (DYnamic Network EVacuation) macroscopic
simulation model, a new version of the IDYNEV model that was developed by KLD under
contract with the Federal Emergency Management Agency (FEMA).
DYNEV II consists of four sub‐models:
A macroscopic traffic simulation model (for details, see Appendix C).
5 The models of the IDYNEV System were recognized as state of the art by the Atomic Safety & Licensing Board (ASLB) in past
hearings. (Sources: Atomic Safety & Licensing Board Hearings on Seabrook and Shoreham; Urbanik). The models have
continuously been refined and extended since those hearings and were independently validated by a consultant retained by the
NRC. The new DYNEV II model incorporates the latest technology in traffic simulation and in dynamic traffic assignment. (Urbanik,
T., et. al. Benchmark Study of the I-DYNEV Evacuation Time Estimate Computer Code, NUREG/CR-4873, Nuclear Regulatory
Commission, June, 1988.)
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A Trip Distribution (TD) model that assigns a set of candidate destination (D) nodes for
each “origin” (O) located within the analysis network, where evacuation trips are
“generated” over time. This establishes a set of O‐D tables.
A Dynamic Traffic Assignment (DTA) model which assigns trips to paths of travel (routes)
which satisfy the O‐D tables, over time. The TD and DTA models are integrated to form
the DTRAD (Dynamic Traffic Assignment and Distribution) model, as described in
Appendix B.
A Myopic Traffic Diversion model which diverts traffic to avoid intense, local congestion,
if possible.
Another software product developed by KLD, named UNITES (UNIfied Transportation
Engineering System) was used to expedite data entry and to automate the production of output
tables.
The dynamics of traffic flow over the network are graphically animated using the software
product, EVAN (EVacuation ANimator), developed by KLD. EVAN is GIS based and displays
statistics such as LOS, vehicles discharged, average speed, and percent of vehicles evacuated,
output by the DYNEV II System. The use of a GIS framework enables the user to zoom in on
areas of congestion and query road name, town name and other geographic information.
The procedure for applying the DYNEV II System within the framework of developing ETE is
outlined in Appendix D. Appendix A is a glossary of terms.
For the reader interested in an evaluation of the original model, I‐DYNEV, the following
references are suggested:
NUREG/CR‐4873 – Benchmark Study of the I‐DYNEV Evacuation Time Estimate
Computer Code.
NUREG/CR‐4874 – The Sensitivity of Evacuation Time Estimates to Changes in Input
Parameters for the I‐DYNEV Computer Code.
The evacuation analysis procedures are based upon the need to:
Route traffic along paths of travel that will expedite their travel from their respective
points of origin to points outside the evacuation region.
Restrict movement toward the wildfire to the extent practicable and disperse traffic
demand so as to avoid focusing demand on a limited number of highways.
Move traffic in directions that are generally outbound relative to the location of the
wildfire.
DYNEV II provides a detailed description of traffic operations on the evacuation network. This
description enables the analyst to identify bottlenecks and to develop countermeasures that
are designed to represent the behavioral responses of evacuees. The effects of these
countermeasures may then be tested with the model.
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Table 1‐1. Stakeholder Interaction
Stakeholder Nature of Stakeholder Interaction
City of Ashland Police Department Attended meetings and engaged in
correspondence to define methodology and data
requirements. Assisted in data collection.
Reviewed and discussed all study assumptions.
City of Ashland Fire Department
City of Ashland Chamber of Commerce
Southern Oregon University (SOU) Attended kick off meeting. Provided SOU data.
Table 1‐2. Highway Characteristics
Number of lanes Posted speed
Lane width Actual free speed
Shoulder type & width Abutting land use
Interchange geometries Control devices
Lane channelization & queuing
capacity (including turn bays/lanes)
Intersection configuration (including
roundabouts where applicable)
Geometrics: curves, grades (>4%) Traffic signal type
Unusual characteristics: Narrow bridges, sharp curves, poor pavement, flood warning
signs, inadequate delineations, toll booths, etc.
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Figure 1‐1. Study Area Location
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Figure 1‐2. Study Area Link‐Node Analysis Network
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2 STUDY ESTIMATES AND ASSUMPTIONS
This section discusses the data estimates and project assumptions utilized in this study. These
assumptions were discussed with representatives from the City of Ashland Police Department.
An assumptions memorandum documenting all the project assumptions was reviewed and
approved by stakeholders from the City of Ashland prior to their use in this study.
2.1 Data Estimates
1. The estimate of permanent resident population was based upon the 2010 U.S. Census
population data from the Census Bureau website1 extrapolated to November 2020 using
annual growth rates that were computed from the 2019 Census population estimates
(see Section 3.1).
2. Estimates of employees who reside outside the EMZ and commute to work within the
EMZ were based upon data obtained from the US Census Longitudinal Employer-
Household Dynamics from the OnTheMap Census analysis tool2.
3. Population estimates at tourist and special facilities were based on the data received
from the City of Ashland Police Department and internet searches (see Sections 3.1.2,
3.3, 3.5 and 3.7).
4. Evacuee mobilization times were based on a statistical analysis of data acquired from a
random sample demographic survey of the EMZ residents conducted in August 2020
(documented in Section 5 and Appendix F).
5. The relationship between permanent resident population and evacuating vehicles was
extracted from the demographic survey. Average values of 2.23 persons per household
(Figure F-1) and 1.43 evacuating vehicles per household (Figure F-10) were used for
permanent resident population. The relationship between persons and vehicles for
other population groups in the EMZs is as follows:
a. Employees: 1.06 employees per vehicle (demographic survey results) for all
major employers.
b. Tourists Population Data (Vehicle Occupancy Average is 2.29 tourists per vehicle;
see Section 3.3 and Appendix E for additional information):
i. Lodging Facilities: Operate at maximum capacity during peak times and
have an average vehicle occupancy of 2.23 persons per vehicle.
ii. Other tourist facilities: Vehicle occupancy varies between 1.00 and 3.57
persons per vehicle.
iii. It was assumed that parking lots are full at peak times, and if no data was
provided, it was assumed that the vehicle occupancy rate is equal to the
average household size, 2.23 persons per vehicle.
6. Roadway capacity estimates were based on field surveys performed in July 2020 and the
application of the Highway Capacity Manual 2016.
1 www.census.gov
2 http://onthemap.ces.census.gov/
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2.2 Study Methodological Assumptions
1. A total of 6 “Scenarios” representing different temporal variations (season, time of day,
day of week) and conditions were considered. These Scenarios are outlined in Table 2-1.
2. Three different wildfire events were considered. The first wildfire evacuation scenario
allows evacuees to travel in any direction. The second wildfire event forces all evacuees
to evacuate towards the south due to a fire originating in the north and traveling
southbound towards the City of Ashland. The third event forces all evacuees to
evacuate towards the north due to a fire originating in the south and moving
northbound towards the City of Ashland.
3. Several sensitivity studies were conducted to determine the elasticity of the evacuation
time estimates based on the notification time distribution.
4. The notification time distribution (the time required for evacuees to receive notification
of an evacuation) is based on the results of the demographic survey. See Section 5.3 for
the notification distribution that is used in this study.
5. The Shadow Region was defined as the area beyond the EMZ including the City of
Talent. The Shadow Region is bounded by the northern city limit of Talent to the north,
the ridge line and the United States Forest Service (USFS) border to the west, a
horizontal line connecting the USFS border to Emigrant Lake to the south, and the ridge
line to the east, see Figure 3-2.
6. The DYNEV II System was used to compute ETE in this study.
7. Evacuees will drive safely, travel away from the wildfire to the extent practicable given
the highway network, and obey all control devices and traffic guides.
8. Evacuation movements (paths of travel) are generally outbound relative to the wildfire
to the extent permitted by the highway network. All major evacuation routes were used
in the analysis.
2.3 Study Assumptions
1. The Planning Basis Assumption for the calculation of ETE is a rapidly escalating hazard
that requires immediate evacuation, and includes the following:
a. Advisory to evacuate is announced coincident with local emergency alerts (NIXLE
Citizen Alert, social media, local news and similar communication systems).
b. Mobilization of the general population will commence within 15 minutes after
the emergency alerts.
c. ETE are measured relative to the advisory to evacuate.
2. One hundred percent (100%) of the people told to evacuate, will do so.
3. Approximately six percent (6%) of the population within the Shadow Region and within
the EMZ not advised to evacuate will voluntarily evacuate based on the results of the
demographic survey performed within the EMZ.
4. Buses will be used to transport those without access to private vehicles:
a. Schools and childcare facilities3
3 Elementary, Middle, and High schools were considered Schools. Anything pre-elementary level was considered a childcare facility.
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i. It was assumed that parents will pick up children at childcare facilities
(pre-elementary schools) prior to evacuation.
ii. School bus demand was computed for all schools based on enrollment
for emergency planning purposes regardless of whether or not parents
will pick up school children prior to evacuating. This will result in a more
conservative estimate of buses and evacuating vehicles.
iii. Schoolchildren, if school is in session, are given priority in assigning
transit vehicles.
b. Medical Facilities
i. Buses, wheelchair transport vehicles and ambulance will be used to
evacuate patients at medical facilities.
c. Transit dependent permanent residents:
i. Transit dependent general population will be evacuated by bus.
ii. Homebound special needs population are included in the transit
dependent population and will be evacuated by bus.
iii. Households with 3 or more vehicles were assumed to have no need for
transit vehicles.
5. Transit vehicle capacities and maximum speed limits:
a. School buses – the study assumed 60 students per bus for elementary school
students, 40 students per bus for middle school and high school students.
b. Ambulatory transit-dependent persons, included college students, and medical
facility patients = 30 people per bus.
c. Basic Life Support (BLS) (ambulances) = 2 persons.
d. Wheelchair transport vehicles – the study assumed 15 persons per wheelchair
bus and 4 persons per wheelchair van.
e. The maximum bus speed assumed is 55 miles per hour, based on the 2019
Oregon Pupil Transportation Manual4.
6. It is assumed all transit vehicles will arrive at the facilities to be evacuated within 90
minutes of the advisory to evacuate.
7. Transit Vehicle loading times:
a. School buses will be loaded in 15 minutes.
b. Transit Dependent buses will require 1 minute of loading time per passenger.
c. Buses for medical facilities will require 1 minute of loading time per ambulatory
passenger.
d. Wheelchair transport vehicles for medical facilities will require 5 minutes of
loading time per passenger.
e. Ambulances for medical facilities will require 30 minutes per bedridden
passenger.
8. The percent breakdown of ambulatory (50%), wheelchair bound (25%) and bedridden
patients (25%) was applied to total populations provided at Asante Ashland Community
4 https://www.oregon.gov/ode/schools-and-districts/ptf/Documents/OPTM%202018%20Draft%20Final.pdf
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Hospital, Linda Vista Nursing & Rehab Center and Ashland Surgery Center, accounting
for rounding errors.
9. It was assumed that drivers for all transit vehicles identified in Table 9-1 are available.
10. Approximately eighty-eight percent (88%) of transit-dependent population will
rideshare with a neighbor or friend, based on the demographic survey results.
11. Vehicles will be traveling through the study area (external-external trips) at the start of a
wildfire. After the advisory to evacuate is announced, these pass-through travelers will
also evacuate. External traffic vehicles will utilize Pacific Highway (I-5) to pass through
the area. Dynamic and variable message signs will be strategically positioned outside of
the hazard area at logical diversion points to attempt to divert traffic away from these
routes. As such, it was assumed this pass-through (external) traffic will diminish over
time with all external traffic flow stopping at 2 hours after the advisory to evacuate.
12. Access control will be implemented on I-5 during an emergency in Ashland. The access
control will be implemented over the course of 2 hours to allow police to mobilize
personnel and equipment to block the roadways and to allow time for commuters to
return home and unite with family (see Section 3.8).
13. Traffic Control Points (TCP) were considered in this analysis. TCPs were considered at
locations that benefit the evacuation during the analysis period. Their number and
location will depend on the Region to be evacuated and resources available. The
objectives of these TCPs are:
a. Facilitate the movements of all (mostly evacuating) vehicles at the location.
b. Discourage inadvertent vehicle movements towards the wildfire.
c. Provide assurance and guidance to any traveler who is unsure of the appropriate
actions or routing.
d. Act as local surveillance and communications center.
e. Provide information to the county and other emergency workers as needed,
based on direct observation or on information provided by evacuees.
14. External Traffic was estimated to be reduced by 60% during evening scenarios (Scenario
3 and 6).
15. This study does not assume that roadways are empty at the start of the first time
period. Rather, there is a 30-minute initialization period (often referred to as “fill time in
traffic simulation) wherein the traffic volumes from the first time period were loaded
onto roadways in the study area. The amount of initialization/fill traffic that is on the
roadways in the study area at the start of the first time period depends on the scenario
and the region being evacuated.
16. Based on the results of the demographic survey, 36 percent of the households in the
EMZ have at least 1 commuter; 39 percent of those households will await the return of
household members before beginning their evacuation trip, based on the demographic
survey results. Therefore, 14 percent (36% x 39% = 14%) of households will await the
return of household members, prior to beginning their evacuation trip.
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Table 2-1. Evacuation Scenario Definitions
Scenarios Season6 Day of Week Time of Day
1 Summer Midweek Midday
2 Summer Weekend Midday
3 Summer Midweek, Weekend Evening
4 Fall Midweek Midday
5 Fall Weekend Midday
6 Fall Midweek, Weekend Evening
6 Fall means that school is in session at normal enrollment levels. Summer means that school is in session at summer school
enrollment levels (lower than normal enrollment).
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3 DEMAND ESTIMATION
This section discusses the estimates of demand, expressed in terms of people and vehicles,
which constitute a critical element in developing an evacuation plan. This section also
documents these sources of data, as well as the methodology used to extract relevant data
from these sources. These estimates consist of three components:
1. An estimate of population within the Emergency Management Zones (EMZ), stratified
into groups (e.g., resident, employee, tourists, special facilities, etc.).
2. An estimate, for each population group, of mean occupancy per evacuating vehicle. This
estimate is used to determine the number of evacuating vehicles.
3. An estimate of potential double‐counting of vehicles.
Appendix E presents much of the source material for the population estimates. Our primary
source of population data, the 2010 Census, however, is not adequate for directly estimating
some tourists.
Throughout the year, vacationers and tourists enter the EMZ. These non‐residents may dwell
within the EMZ for a short period (e.g., a few days or one or two weeks), or may enter and
leave within one day. Estimates of the size of these population components must be obtained,
so that the associated number of evacuating vehicles can be ascertained.
The potential for double‐counting people and vehicles must be addressed. For example:
A resident who works and shops within the EMZ could be counted as a resident, again as
an employee and once again as a shopper.
A visitor who stays at a hotel and spends time at a park, then goes shopping could be
counted three times.
Furthermore, the number of vehicles at a location depends on time of day. For example, motel
parking lots may be full at dawn and empty at noon. Similarly, parking lots at area parks, which
are full at noon, may be almost empty at dawn. Estimating counts of vehicles by simply adding
up the capacities of different types of parking facilities will tend to overestimate the number of
tourists and can lead to ETE that are too conservative.
Analysis of the population characteristics of the study area indicates the need to identify three
distinct groups:
Permanent residents ‐ people who are year‐round residents of the EMZ.
Tourists ‐ people who reside outside of the EMZ who enter the area for a specific
purpose (shopping, recreation) and then leave the area.
Employees ‐ people who reside outside of the EMZ and commute to work within the
EMZ on a daily basis.
Estimates of the population and number of evacuating vehicles for each of the population
groups are presented for each EMZ. The EMZ boundaries are shown in Figure 3‐1.
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3.1 Permanent Residents
The primary source for estimating permanent population is the latest U.S. Census data. The U.S.
Census Bureau conducts a physical census of the permanent resident population in the U.S.
every ten years. The last census began on April 1, 2010 with data from the census being
published on April 1, 2011. In the years between the decennial censuses, the Census Bureau
works with state and local agencies to provide annual population estimates at the state and
local levels. These estimates are done using data on deaths, births and migration. This annual
data gathering process and analysis is extensive. As such, population estimates are a year
behind – 2019 data are released in 20201.
This study is based on 2010 Census population data from the Census Bureau website2
extrapolated to 2020 using annual growth rates computed from the 2019 Census population
estimates as outlined in the methodology below.
The Census Bureau QuickFacts3 website provides annual population estimates for each state,
county, and municipality4 in the United States. As discussed above, Census population
estimates are a year behind. Thus, the most recent population estimates available for the
counties and municipalities are for the time period from April 1, 2010 to July 1, 2019. The
population change and annual growth rate for each county and municipality in the study area
(the EMZ plus Shadow Region) are provided in Table 3‐1 and Table 3‐2, respectively. Figure 3‐2
shows the county and municipality boundaries identified by the Census Bureau.
The permanent resident population, as per the 2010 Census, for the EMZ and the Shadow
Region was projected to 2020 using the compound growth formula (Equation 1). In the
compound growth formula, g is the annual growth rate and X is the number of years projected
forward from Year 2010. The compound growth formula can be solved for g as shown in
Equation 2.
Equation 1
ሺ𝐶𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝐺𝑟𝑜𝑤𝑡ℎ 𝑓𝑜𝑟 𝑋 𝑦𝑒𝑎𝑟𝑠ሻ: 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 201𝑋 ൌ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 2010 ሺ1 𝑔ሻ ௫
Equation 2
ሺ𝑆𝑜𝑙𝑣𝑖𝑛𝑔 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑔𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒ሻ: 𝑔ൌ ሺ𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 201𝑋 ൊ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 2010ሻଵ/௫ – 1
The 2010 and 2019 population data provided in Table 3‐1 and Table 3‐2 were used in Equation
2 to compute the annual growth rate for each county and municipality in the study area using X
= 9.25 (9 years and 3 months from April 1, 2010 to July 1, 2019). The computed annual growth
rate for each county and municipality is summarized in the final column of Table 3‐1 and Table
3‐2, respectively.
1 The schedule for release of Census data is provided on the Census website: https://www.census.gov/programs-
surveys/popest/about/schedule.html 2 www.census.gov
3 https://www.census.gov/quickfacts/fact/table/US/PST045218
4 https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-cities-and-towns.html
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The most detailed data should always be used when forecasting population. In terms of
detailed data, municipal data is the finest level of detail, then county data, and state data. The
municipality growth rate was used first and if that was not available or applicable within the
study area, then the county growth rate was used. County growth rates are available for the
entire study area and were used (in the absence of municipal data) as they are the finest level
of detail available for the entire study area. Thus, state data was not used.
The Census Bureau does not provide population data specific to the boundaries of the study
area. As such, the county or municipality population was used to compute the annual growth
rate. Then, the appropriate municipality or county growth rate was applied only to those
Census blocks located within the study area. All other blocks outside of the study area were not
considered as part of the EMZ or Shadow Region population, even if they are located within
one of the municipalities or counties that intersect the study area.
The appropriate annual growth rate was applied to each Census block in the study area
depending on which county or municipality the block is located within. The population was
extrapolated to November 1, 2020 using Equation 1 with X = 10.58 (10 years and 7 months from
the April 1, 2010 Census date to November 1, 2020), as the base year for this study.
The permanent resident population is estimated by cutting the census block polygons by the
EMZ boundaries. A ratio of the original area of each census block and the updated area (after
cutting) is multiplied by the total block population to estimate what the population is within the
EMZ. This methodology (referred to as the “area ratio method”) assumes that the population is
evenly distributed across a census block. Table 3‐3 provides the permanent resident population
within the EMZ for 2010 (based on the most recent U.S. Census) and for 2020 (based on the
methodology above). As indicated, the permanent resident population within the EMZ has
increased by approximately 6.83% since the 2010 Census.
The 2020 extrapolated permanent resident population is divided by the average household size
and then multiplied by the average number of evacuating vehicles per household to estimate
number of vehicles. The average household size (2.23 persons/household) was estimated using
the demographic survey results (see Appendix F, sub‐section F.3.1). The number of evacuating
vehicles per household (1.43 vehicles/household – See Appendix F, sub‐section F.3.2) was also
adapted from the demographic survey results. Permanent resident population and vehicle
estimates are presented in Table 3‐4.
It can be argued that this estimate of permanent residents overstates, somewhat, the number
of evacuating vehicles, especially during the summer. It is certainly reasonable to assert that
some portion of the population would be on vacation during the summer and would travel
elsewhere. A rough estimate of this reduction can be obtained as follows:
Assume 50 percent of all household’s vacation for a two‐week period over the summer.
Assume these vacations, in aggregate, are uniformly dispersed over 10 weeks, i.e. 10
percent of the population is on vacation during each two‐week interval.
Assume half of these vacationers leave the area.
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On this basis, the permanent resident population would be reduced by 5 percent in the summer
and by a lesser amount in the off‐season. Given the uncertainty in this estimate, we elected to
apply no reductions in permanent resident population for the summer scenarios to account for
residents who may be out of the area.
3.1.1 Special Facilities
Three medical facilities are located within the EMZ (see Table E‐2). These facilities have
permanent residents that are included in the Census; however, these facilities are transit
dependent (will not evacuate in personal vehicles) and are addressed below in Section 3.5. As
such, these residents are included in the resident population, but no personal evacuating
vehicles are considered. The vehicles in Table 3‐4 have been adjusted accordingly.
3.1.2 University Students
Southern Oregon University (SOU) is the only university in the EMZ. Upon examination for
Census blocks in the vicinity of these campuses, it does not appear the resident students were
captured in the Census. As such, no modifications to residents or resident vehicles were made
to account for this university.
Based on the data provided by the City of Ashland, some students will evacuate in private
vehicles while other students either rideshare with a fellow classmate or need transportation
assistance (a bus) to evacuate. The campuses are broken down as follows.
According to the city, the total enrollment of SOU is 5,000.
According to the National Application Center database5, 83% of students live off campus
and 17% of students live on campus. Sixty (60%) of students who live on campus own
personal vehicles. Therefore, 4,150 (5,000 x 83%) students live off campus, 850 (5,000 x
17%) students live on campus, and 510 (850 x 60%) on campus students own personal
vehicles. In other words, 510 personal vehicles will be used by students living on campus
during an evacuation.
Based on the demographic survey results, the commuter vehicle occupancy is 1.06
persons per vehicle (see Appendix F, subsection F.3.1). Thus, the number of commuter
vehicles used by off‐campus students is 3,915 (4,150 ÷ 1.06).
The demographic survey results indicate 88% of the transit‐dependent people will
rideshare with a neighbor or friend (see Appendix F, subsection F.3.1). Apply this ratio to
the 340 (850 – 510) on‐campus students with no personal vehicles, resulting in 299 (340
x 88%) students who will rideshare to evacuate. In summary, the total number of on‐
campus students evacuated by personal vehicles is 809 (510 + 299). This leaves 41 (850
– 809) on‐campus students who are transit dependent and need buses to evacuate.
5
https://www.nationalapplicationcenter.com/gotocollege/campustour/undergraduate/4790/Southern_Oregon_University/Southern_Or
egon_University5.html
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Using the capacity of 30 people per bus (see Section 2.3, Assumption 5), SOU needs 2
(41 ÷ 30 = 2, rounded up) transit‐dependent buses or 4 passage car equivalent (pce’s)
vehicles (1 bus is equivalent to 2 passenger vehicles).
In summary, 4,959 (809 + 4,150) commuter/ridesharing students will be evacuated in
4,425 (510 + 3,915) private vehicles, and 41 transit‐dependent students will be
evacuated in 2 buses.
3.2 Shadow Population
A portion of the population living outside the evacuation area, including the City of Talent, may
elect to evacuate without having been instructed to do so. Based on the demographic survey, it
is assumed that 6 percent of the permanent resident population, based on U.S. Census Bureau
data, in this Shadow Region will elect to evacuate.
Shadow population characteristics (household size, evacuating vehicles per household,
mobilization time) are assumed to be the same as that of the permanent resident population.
There are 10,099 permanent residents and 6,490 vehicles in the Shadow Region.
3.3 Tourist Population
Tourist population groups are defined as those people (who are not permanent residents, nor
commuting employees) who enter the EMZ for a specific purpose (shopping, recreation).
Tourists may spend less than one day or stay overnight at hotels and motels. Data was provided
by the City of Ashland for the majority of these facilities. For facilities wherein no data was
provided or data was not available at that time, parking lot spaces were used to estimate
facility capacities, see Section 2.1, Assumption 5b. Vehicle occupancy rates vary by facility from
1.00 persons per vehicle to 3.57 persons per vehicle. The EMZ has a number of areas and
facilities that attract tourists, including:
Golf Courses
Hiking Trails
Lodging Facilities
Theatres
There is one golf course within the EMZ. According to the city, both the average daily peak
attendance and parking capacity of this facility are 50. Therefore, an average of 1.00 person per
vehicle is assigned to this facility.
There are four hiking trails within the EMZ. The average number of daily hikers and parking
capacity for each hiking trail was provided by the city. There are limited parking spots for
Oredson Todd Woods and Acid Castle Boulders Access Point. It is assumed the hikers go hiking
in pairs (2.00 persons per vehicles) and their vehicles are parked in the nearby areas when the
parking spaces are fully occupied. A total of 1,435 tourists in 558 vehicles (an average of 2.57
persons per vehicle) are assigned to the hiking trails in the EMZ.
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There are fourteen lodging facilities within the EMZ. The average daily capacity for each lodging
facility was provided by the city. Assumed the tourists travel as groups of family member, the
average household size (2.23 persons/household) was used to estimate the tourist vehicles. A
total of 1,690 tourists in 758 vehicles (an average of 2.23 persons per vehicle) are assigned to
the lodging facilities in the EMZ.
According to the city, a theatre festival – Oregon Shakespeare Festival runs from February to
October each year. The average daily peak attendance is 2,500, and 21% of the visitors are
residents living within the EMZ. The remaining 1,975 (2,500 x 79%) visitors live outside of the
EMZ. Assuming these visitors attend the festival with family members, the average household
size (2.23 persons/household) was used to estimate the vehicles for the festival. Thus, 1,975
tourists in 886 vehicles (an average of 2.23 persons per vehicle) are assigned to the theatre.
Appendix E summarizes the tourist data that was estimated for the EMZ. Table E‐4 presents the
number of tourists visiting recreational facilities and lodging facilities within the EMZ.
In total, there are 5,150 tourists evacuating in 2,252 vehicles (an average of 2.29 tourists per
vehicle) in the study area. Table 3‐5 presents tourist population and vehicle estimates in the
study area.
3.4 Employees
Employees who work within the EMZ fall into two categories:
Those who live and work in the EMZ
Those who live outside of the EMZ and commute to jobs within the EMZ
Those of the first category are already counted as part of the permanent resident
population. To avoid double counting, we focus only on those employees commuting from
outside the EMZ who will evacuate along with the permanent resident population.
Data obtained from the US Census Longitudinal Employer‐Household Dynamics from the
OnTheMap Census analysis tool6 were used to estimate the number of employees commuting
into the EMZ. The latest Workplace Area Characteristic data available (2017), was also obtained
from this website and was used to determine the number of employees by Census Block within
the EMZ.
Since not all employees are working at facilities within the EMZ at one time, a maximum shift
reduction was applied. The Work Area Profile Report, also output by the OnTheMap
Application, breaks down jobs within the EMZ by industry sector. Assuming maximum shift
employment occurs Monday through Friday between 9 AM and 5 PM, the following jobs take
place outside the typical 9‐5 workday:
Manufacturing – 5.7% of jobs; takes place in shifts over 24 hours
Arts, Entertainment, and Recreation – 9.1% of jobs; takes place in evenings and on
weekends
6 http://onthemap.ces.census.gov/
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Accommodations and Food Services – 26.2% of jobs; peaks in the evenings
The maximum shift in the EMZ is about 59.0% (100% ‐ 5.7% ‐ 9.1% ‐ 26.2% = 59.0%). This value
was applied to the total employment in 2017 to represent the maximum number of employees
present in the EMZ at any one time. The Inflow/Outflow Report was then used to calculate the
percent of employees that work within the EMZ but live outside. This value, 66.8%, was applied
to the maximum shift employee values to compute the number of people commuting into the
EMZ to work at peak times. Table E‐3 in Appendix E summarizes the number of employees
commuting into the EMZ during the peak shift.
In Table 3‐6, a vehicle occupancy of 1.06 employees per vehicle obtained from the demographic
survey (See Appendix F, sub‐section F.3.1, “Commuter Travel Modes”) was used to determine
the number of evacuating employee vehicles for all major employers. Table 3‐6 presents
employee and vehicle estimates by EMZ.
3.5 Medical Facilities
The data for the three medical facilities was provided by the city. Table E‐2 in Appendix E
summarizes the data gathered. Table 3‐7 presents the current census and transportation
requirement of medical facilities in the EMZ. As shown in these tables, 250 people have been
identified as living in, or being treated in, these facilities. Since the average number of patients
at medical facilities fluctuate daily, a percent breakdown of ambulatory, wheelchair bound, and
bedridden patients was used to estimate the number of each type of patient (see Section 2.3,
Assumption 8). The estimated breakdown for the three facilities consists of about 50%
ambulatory, 25% wheelchair bound, and 25% bedridden patients, accounting for rounding
errors. The number of ambulances is determined by assuming that 2 patients can be
accommodated per ambulance trip; the number of wheelchair buses assumes 15 wheelchairs
per trip, and the number of buses estimated assumes 30 ambulatory patients per trip (see
Section 2.3, Assumption 5).
3.6 Transit Dependent Population
The demographic survey results were used to estimate the portion of the population requiring
transit service:
• Those persons in households that do not have a vehicle available.
• Those persons in households that do have vehicle(s) that would not be available at
the time the evacuation is advised.
In the latter group, the vehicle(s) may be used by a commuter(s) who does not return (or is not
expected to return) home to evacuate the household.
Table 3‐8 presents estimates of transit‐dependent people. Note:
• Estimates of persons requiring transit vehicles include schoolchildren. For those
evacuation scenarios where children are at school when an evacuation is ordered,
separate transportation is provided for the schoolchildren. The actual need for
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transit vehicles by residents is thereby less than the given estimates. However,
estimates of transit vehicles are not reduced when schools are in session.
• It is reasonable and appropriate to consider that many transit‐dependent persons
will evacuate by ride‐sharing with neighbors, friends or family. For example, nearly
80 percent of those who evacuated from Mississauga, Ontario7 who did not use their
own cars, shared a ride with neighbors or friends. Other documents report that
approximately 70 percent of transit dependent persons were evacuated via ride
sharing. The results from the demographic survey indicate approximately 88 percent
is appropriate for this area. As such, 88 percent ride‐sharing was utilized to estimate
the transit dependent population within the EMZ.
The estimated number of bus trips needed to service transit‐dependent persons is based on an
estimate of average bus occupancy of 30 persons at the conclusion of the bus run. Transit
vehicle seating capacities typically equal or exceed 60 children on average (roughly equivalent
to 40 adults). If transit vehicle evacuees are two thirds adults and one third children, then the
number of “adult seats” taken by 30 persons is 20 + (2/3 x10) = 27. On this basis, the average
load factor anticipated is (27/40) x 100 = 68 percent. Thus, if the actual demand for service
exceeds the estimates of Table 3‐8 by 50 percent, the demand for service can still be
accommodated by the available bus seating capacity.
20 ൬2
3 ൈ 10൰൨ൊ40 ൈ 1.5 ൌ 1.00
Table 3‐8 indicates that transportation must be provided for 94 people. Therefore, a total of 4
buses are required to transport this population outside of the EMZ.
To illustrate this estimation procedure, we calculate the number of persons, P, requiring public
transit or ride‐share, and the number of buses, B, required for the EMZ:
𝑃ൌ𝑁𝑜.𝑜𝑓 𝐻𝐻 ൈሼሺ% 𝐻𝐻 𝑤𝑖𝑡ℎ 𝑖 𝑣𝑒ℎ𝑖𝑐𝑙𝑒𝑠ሻ ൈ ሾሺ𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐻𝐻 𝑆𝑖𝑧𝑒ሻ െ𝑖ሿሽ
ୀ
ൈ𝐴𝐶
Where,
A = Percent of households with commuters
C = Percent of households who will not await the return of a commuter
𝑃ൌ9,618 ൈ ሾ1.50 ൈ 0.0068 0.3530 ൈ ሺ1.76 െ 1ሻ ൈ 0.3586 ൈ 0.6131
0.4426 ൈ ሺ2.56 െ 2ሻ ൈ ሺ0.3582 ൈ 0.6131ሻଶሿ ൌ 780
𝐵ൌሺሺ1 െ 0.88ሻൈ𝑃ሻ ൊ 30 ൌ 4
7 1979 Mississauga Train Derailment
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These calculations are explained as follows:
• Number of households is computed by dividing the EMZ population (21,449) by the
average household size (2.23) and equates to 9,618.
• All members (1.50 avg.) of households (HH) with no vehicles (0.68%) will evacuate by
public transit or ride‐share. The term 9,618 (number of households) x 1.50 x 0.0068,
accounts for these people.
• The members of HH with 1 vehicle (35.30%) away, who are at home, equal (1.76‐1).
The number of HH where the commuter will not return home is equal to (9,618 x
0.76 x 0.3530 x 0.3582 x 0.6131), as 35.82% of EMZ households have a commuter,
61.31% of which would not return home in the event of an emergency. The number
of persons who will evacuate by public transit or ride‐share is equal to the product
of these two terms.
• The members of HH with 2 vehicles (44.26%) that are away, who are at home, equal
(2.56 – 2). The number of HH where neither commuter will return home is equal to
9,618 x 0.4426 x 0.56 x (0.3582 x 0.6131)2. The number of persons who will evacuate
by public transit or ride‐share is equal to the product of these two terms (the last
term is squared to represent the probability that neither commuter will return).
• Households with 3 or more vehicles are assumed to have no need for transit
vehicles.
• The total number of persons requiring public transit is the sum of such people in HH
with 1 or 2 vehicles that are away from home and households with no vehicles.
It is assumed that homeless people and those with access and functional needs who may also
need assistance and do not reside in medical facilities are included in these calculations. Data
was not provided on the homeless population or those with access and functional needs.
KLD designed bus routes to service the transit dependent population in each EMZ. These routes
are shown in Figure 11‐2 and described in Table 11‐1. These routes were designed by grouping
EMZs into clusters to minimize the number of buses needed. For example, using a weighted
distribution, there are 5 people in EMZ 1, 8 people in EMZ 2, and 9 people in EMZ 10 that would
need transportation assistance to evacuate. Since these EMZs border one another, a single bus
could be used to gather all of these people. Assuming a bus capacity of 30 people, as discussed
above, only one bus is needed to evacuate these three EMZs, rather than using one bus for
each EMZ. This grouping of EMZs should be considered when looking at the summary of
vehicle demand by EMZ at the end of this section.
3.7 School Population Demand
Table 3‐9 presents the school population and transportation requirements for the direct
evacuation of all schools within the EMZ. This information was provided by the City of Ashland
supplemented by internet searches for schools in which no data was provided. The column in
Table 3‐9 entitled “Buses Required” specifies the number of buses required for each school
under the following set of assumptions and estimates:
• No students will be picked up by their parents prior to the arrival of the buses.
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• The estimate of buses required for school evacuation does not consider the use of
private vehicles by students.
• Bus capacity, expressed in students per bus, was assumed to be 40 for High School
and Middle School buses, and 60 for Elementary School and childcare facility buses.
• Those staff members who do not accompany the students will evacuate in their
private vehicles.
• No allowance is made for student absenteeism, typically 3 percent daily.
The City of Ashland may consider procedures whereby the schools are contacted prior to the
dispatch of buses from the depot to ascertain the current estimate of students to be evacuated.
In this way, the number of buses dispatched to the schools will reflect the actual number
needed. The need for buses would be reduced by any high school students who have evacuated
using private automobiles (if permitted by school authorities). Those buses originally allocated
to evacuate schoolchildren that are not needed due to children being picked up by their
parents, can be gainfully assigned to service other facilities or those persons who do not have
access to private vehicles or to ride‐sharing.
3.8 External Traffic
Vehicles will be traveling through the study area (external‐external trips) at the time of an
event. After the Advisory to Evacuate is announced, these through‐travelers will also evacuate.
These through vehicles are assumed to travel on the major route traversing the study area –
Interstate 5. It is likely dynamic and variable message signs will be strategically positioned
outside of the study area at logical diversion points to attempt to divert traffic away from the
area at risk. As such, it is assumed this external traffic will diminish over 120 minutes following
the Advisory to Evacuate.
Average Annual Daily Traffic (AADT) data was obtained from Oregon Department of
Transportation (ODOT) to estimate the number of vehicles per hour on the aforementioned
routes. The AADT was multiplied by the K‐Factor, which is the proportion of the AADT on a
roadway segment or link during the design hour, resulting in the Design Hour Volume (DHV).
The design hour is usually the 30th highest hourly traffic volume of the year, measured in
vehicles per hour (vph). The DHV is then multiplied by the D‐Factor, which is the proportion of
the DHV occurring in the peak direction of travel (also known as the directional split).
The resulting values are the directional design hourly volumes (DDHV) and are presented in
Table 3‐10, for each of the routes considered. The DDHV is then multiplied by 2 hours (dynamic
messaging signs are assumed to be activated within the 120 minutes of the ATE; no vehicles
have diverted during this time) to estimate the total number of external vehicles loaded on the
analysis network. As indicated in Table 3‐10, there are 8,412 vehicles entering the study area as
external‐external trips prior to any diversion of traffic. This number is reduced by 60% for
evening scenarios (Scenarios 3 and 6) as discussed in Section 6.
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3.9 Background Traffic
Section 5 discusses the time needed for the people in the study area to mobilize and begin their
evacuation trips. As shown in Table 5‐8, there are 14 time periods during which traffic is loaded
on to roadways in the study area to model the mobilization time of people in the study area. All
traffic is loaded within these 14 time periods. Note, there is no traffic generated during the 15th
time period, as this time period is intended to allow traffic that has already begun evacuating to
clear the study area boundaries.
In traffic simulations, the network is initially empty. Thus, for this study, the network needs to
be filled (to represent a routine travel conditions just prior to an evacuation order) so that
system performance can be assessed under a more realistic set of conditions. As such, there is a
30‐minute initialization time period (often referred to as “fill time” in traffic simulation)
wherein a portion of the traffic volumes from Time Period 1 are loaded onto roadways in the
study area. The amount of initialization/fill traffic that is on the roadways in the study area at
the start of Time Period 1 depends on the scenario and the region being evacuated (see Section
6). There are 1,578 vehicles on the roadways in the study area at the end of fill time for an
evacuation of all the EMZ (Region R18) under Scenario 1 (summer, midweek, midday, normal)
conditions.
3.10 Summary of Demand
A summary of population and vehicle demand is provided in Table 3‐11 and Table 3‐12,
respectively. This summary includes all population groups described in this section. A total of
47,674 people and 37,654 vehicles are considered in this study.
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Table 3‐1. County Population Change and Annual Growth Rate from April 1, 2010 to July 1, 2019
County 2010 Population 2019 Population Percent Change Annual Growth Rate
Jackson 203,204 220,944 8.73% 0.91%
Table 3‐2. Municipality Population Change and Annual Growth Rate from April 1, 2010 to July 1, 2019
Municipality 2010 Population 2019 Population Percent Change Annual Growth Rate
Jackson County, OR
EMZ
Ashland 20,076 21,281 6.00% 0.63%
Shadow Region
Talent 6,059 6,608 9.06% 0.94%
Table 3‐3. EMZ Permanent Resident Population
EMZ 2010 Population
2020 Extrapolated
Population
1 1,170 1,252
2 1,665 1,777
3 3,283 3,506
4 2,653 2,835
5 593 636
6 3,307 3,531
7 1,973 2,110
8 1,397 1,489
9 2,115 2,257
10 1,922 2,056
TOTAL 20,078 21,449
Population Growth (2010‐2020): 6.83%
Shadow 9,164 10,099
STUDY AREA TOTAL 29,242 31,548
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Table 3‐4. Permanent Resident Population and Vehicles by EMZ
EMZ
2020 Extrapolated
Population
2020
Resident Vehicles
1 1,252 803
2 1,777 1,074
3 3,506 2,243
4 2,835 1,813
5 636 409
6 3,531 2,266
7 2,110 1,354
8 1,489 953
9 2,257 1,445
10 2,056 1,306
TOTAL 21,449 13,666
Shadow 10,099 6,490
STUDY AREA TOTAL 31,548 20,156
Table 3‐5. Summary of Tourists and Tourist Vehicles
EMZ Tourists Tourist Vehicles
1 1,000 280
2 75 1338
3 2,205 4208
4 180 90
5 732 355
6 328 147
7 266 119
8 0 4758
9 180 150
10 184 83
TOTAL 5,150 2,252
8 There is limited parking capacity at Oregon Shakespeare Festival in EMZ 3. According to the city, the vehicles for the festival are
parked at multiple places near the theatre in EMZs (2, 3 and 8).
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Table 3‐6. Summary of Employees and Employee Vehicles Commuting into the EMZ
EMZ Employees Employee Vehicles
1 74 70
2 100 94
3 627 593
4 0 0
5 181 171
6 475 448
7 86 81
8 599 565
9 54 51
10 106 100
TOTAL 2,302 2,173
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Table 3‐7. Medical Facilities Transit Demand Estimates
EMZ Facility Name Capacity
Current
Census
Ambulatory
Patients
Wheel‐
chair
Bound
Patients
Bed‐
ridden
Patients Buses
Wheel‐
chair
Buses Ambulances
2 Asante Ashland Community Hospital 125 125 63 31 31 3 3 16
10 Ashland Surgery Center 25 25 13 6 6 1 1 3
2 Linda Vista Nursing & Rehab Center 100 100 50 25 25 2 2 13
TOTAL: 250 250 126 62 62 6 6 32
Table 3‐8. Transit‐Dependent Population Estimates
Projected
2020 EPZ
Population
Survey Average
HH Size
with Indicated No.
of Vehicles
Estimated
No. of
Households
Survey Percent HH
with Indicated No. of
Vehicles
Survey
Percent HH
with
Commuters
Survey
Percent HH
with Non‐
Returning
Commuters
Total
People
Requiring
Transport
Estimated
Ridesharing
Percentage
People
Requiring
Public
Transit
Percent
Population
Requiring
Public
Transit 0 1 2 0 1 2
21,449 1.50 1.76 2.56 9,618 0.68% 35.30% 44.26% 35.82% 61.31% 780 88% 94 0.4%
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Table 3‐9. School and Preschool/Daycare Population Demand Estimates
EMZ School Name Enrollment
Buses
Required
10 Helman Elementary School 360 6
7 Ashland High School 1,000 25
6 Walker Elementary School 340 6
6 Ashland Middle School 900 23
6 John Muir Elementary School 270 5
6 Bellview Elementary School 460 8
3 Southern Oregon University 5,000 2
TOTAL: 8,330 75
2 Oregon Child Development 213 4
3 Reflective Hearts Childcare 10 1
6 Stone Soup Playschool 10 1
3 Pea Pod Village 10 1
2 Children’s World 10 1
4 Memory Lane Preschool 20 1
7 Head Start 20 1
4 Rain and Shine Preschool 20 1
PRESCHOOL/DAYCARE TOTAL: 313 11
Table 3‐10. Study Area External Traffic Demand
Upstream
Node
Downstream
Node Road Name Direction ODOT1 K‐Factor2 D‐Factor2 Hourly
Volume
External
AADT Traffic
8001 291 I‐5 NB 39,300 0.107 0.5 2,103 4,206
8002 31 I‐5 SB 39,300 0.107 0.5 2,103 4,206
TOTAL: 8,412
1 https://www.oregon.gov/ODOT/DATA/Pages/Traffic‐Counting.aspx
2 HCM 2016
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Table 3‐11. Summary of Population Demand
EMZ Residents
Transit‐
Dependent Tourists Employees
Medical
Facilities Schools
On
Campus
University
Students
Off
Campus
University
Students
External
Traffic Total
1 1,252 5 1,000 74 0 0 0 0 0 2,331
2 1,777 8 75 100 225 0 0 0 0 2,185
3 3,506 15 2,205 627 0 0 850 4,150 0 11,353
4 2,835 15 180 0 0 0 0 0 0 3,030
5 636 3 732 181 0 0 0 0 0 1,552
6 3,531 13 328 475 0 1,970 0 0 0 6,317
7 2,110 9 266 86 0 1,000 0 0 0 3,471
8 1,489 7 0 599 0 0 0 0 0 2,095
9 2,257 10 180 54 0 0 0 0 0 2,501
10 2,056 9 184 106 25 360 0 0 0 2,740
Shadow 10,099 0 0 0 0 0 0 0 0 10,099
Total 31,548 94 5,150 2,302 250 3,330 850 4,150 0 47,674
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Table 3‐12. Summary of Vehicle Demand
EMZ Residents
Transit‐
Dependent10 11 Tourists Employees
Medical
Facilities10
School
Buses10
On
Campus
University
Students
Off
Campus
University
Students
External
Traffic Total
1 803 2 280 70 0 0 0 0 0 1,155
2 1,074 0 133 94 49 10 0 0 0 1,360
3 2,243 2 420 593 0 8 510 3,915 0 7,691
4 1,813 0 90 0 0 4 0 0 0 1,907
5 409 2 355 171 0 0 0 0 0 937
6 2,266 0 147 448 0 86 0 0 0 2,947
7 1,354 2 119 81 0 52 0 0 0 1,608
8 953 0 475 565 0 0 0 0 0 1,993
9 1,445 0 150 51 0 0 0 0 0 1,646
10 1,306 0 83 100 7 12 0 0 0 1,508
Shadow 6,490 0 0 0 0 0 0 0 8,412 14,902
Total 20,156 8 2,252 2,173 56 172 510 3,915 8,412 37,654
10 One bus is equivalent to 2 pce’s. As such, buses for transit dependent persons, ambulatory and wheelchair bound medical facility patients, schools and colleges are doubled in the
simulation.
11 Transit dependent buses for EMZs 2 and 10 are included with EMZ 1. Transit dependent buses for EMZ 4 is included with EMZ 3. Transit dependent buses for EMZ 6 is included
with EMZ 5. Transit dependent buses for EMZs 8 and 9 are included with EMZ 7.
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Figure 3‐1. EMZ Boundaries
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Figure 3‐2. Census Boundaries within the Study Area
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4 ESTIMATION OF HIGHWAY CAPACITY
The ability of the road network to service vehicle demand is a major factor in determining how
rapidly an evacuation can be completed. The capacity of a road is defined as the maximum
hourly rate at which persons or vehicles can reasonably be expected to traverse a point or
uniform section of a lane of roadway during a given time period under prevailing roadway,
traffic and control conditions, as stated in the 2016 Highway Capacity Manual (HCM 2016). This
section discusses how the capacity of the roadway network was estimated.
In discussing capacity, different operating conditions have been assigned alphabetical
designations, A through F, to reflect the range of traffic operational characteristics. These
designations have been termed "Levels of Service" (LOS). For example, LOS A connotes
free‐flow and high‐speed operating conditions; LOS F represents a forced flow condition. LOS E
describes traffic operating at or near capacity.
Another concept, closely associated with capacity, is “Service Volume” (SV). Service volume is
defined as “The maximum hourly rate at which vehicles, bicycles or persons reasonably can be
expected to traverse a point or uniform section of a roadway during an hour under specific
assumed conditions while maintaining a designated level of service.” This definition is similar to
that for capacity. The major distinction is that values of SV vary from one LOS to another, while
capacity is the service volume at the upper bound of LOS E, only.
Thus, in simple terms, a service volume is the maximum traffic that can travel on a road and still
maintain a certain perceived level of quality to a driver based on the A, B, C, rating system
(LOS). Any additional vehicles above the service volume would drop the rating to a lower letter
grade.
This distinction is illustrated in Exhibit 12‐37 of the HCM 2016. As indicated there, the SV varies
with Free Flow Speed (FFS), and LOS. The SV is calculated by the DYNEV II simulation model,
based on the specified link attributes, FFS, capacity, control device and traffic demand.
Other factors also influence capacity. These include, but are not limited to:
Lane width
Shoulder width
Pavement condition
Horizontal and vertical alignment (curvature and grade)
Percent truck traffic
Control device (and timing, if it is a signal)
Weather conditions (rain, snow, fog, wind speed, ice)
These factors are considered during the road survey and in the capacity estimation process;
some factors have greater influence on capacity than others. For example, lane and shoulder
width have only a limited influence on Base Free Flow Speed (BFFS1) according to Exhibit 15‐7
of the HCM. Consequently, lane and shoulder widths at the narrowest points were observed
1 A very rough estimate of BFFS might be taken as the posted speed limit plus 10 mph (HCM 2016 Page 15-15).
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during the road survey and these observations were recorded, but no detailed measurements
of lane or shoulder width were taken. Horizontal and vertical alignment can influence both FFS
and capacity. The estimated FFS were measured using the survey vehicle’s speedometer and
observing local traffic, under free flow conditions. Capacity is estimated from the procedures of
the 2016 HCM. For example, HCM Exhibit 7‐1(b) shows the sensitivity of Service Volume at the
upper bound of LOS D to grade (capacity is the Service Volume at the upper bound of LOS E).
The amount of traffic that can flow on a roadway is effectively governed by vehicle speed and
spacing. The faster that vehicles can travel when closely spaced, the higher the amount of flow.
Since congestion arising from evacuation may be significant, estimates of roadway capacity
must be determined with great care. Because of its importance, a brief discussion of the major
factors that influence highway capacity is presented in this section.
Rural highways generally consist of: (1) one or more uniform sections with limited access
(driveways, parking areas) characterized by “uninterrupted” flow; and (2) approaches to at‐
grade intersections where flow can be “interrupted” by a control device or by turning or
crossing traffic at the intersection. Due to these differences, separate estimates of capacity
must be made for each section. Often, the approach to the intersection is widened by the
addition of one or more lanes (turn pockets or turn bays), to compensate for the lower capacity
of the approach due to the factors there that can interrupt the flow of traffic. These additional
lanes are recorded during the field survey and later entered as input to the DYNEV II system.
4.1 Capacity Estimations on Approaches to Intersections
At‐grade intersections are apt to become the first bottleneck locations under local heavy traffic
volume conditions. This characteristic reflects the need to allocate access time to the respective
competing traffic streams by exerting some form of control. During evacuation, control at
critical intersections will often be provided by traffic control personnel assigned for that
purpose, whose directions may supersede traffic control devices.
The per‐lane capacity of an approach to a signalized intersection can be expressed
(simplistically) in the following form:
𝑄, ൌ ൬3600
ℎ
൰ ൈ ൬𝐺െ𝐿
𝐶൰
ൌ ൬3600
ℎ
൰ ൈ𝑃
where:
Qcap,m = Capacity of a single lane of traffic on an approach, which executes
movement, m, upon entering the intersection; vehicles per hour (vph)
hm = Mean queue discharge headway of vehicles on this lane that are executing
movement, m; seconds per vehicle
G = Mean duration of GREEN time servicing vehicles that are executing
movement, m, for each signal cycle; seconds
L = Mean "lost time" for each signal phase servicing movement, m; seconds
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C = Duration of each signal cycle; seconds
Pm = Proportion of GREEN time allocated for vehicles executing movement, m,
from this lane. This value is specified as part of the control treatment.
m = The movement executed by vehicles after they enter the
intersection: through, left‐turn, right‐turn, and diagonal.
The turn‐movement‐specific mean discharge headway hm, depends in a complex way upon
many factors: roadway geometrics, turn percentages, the extent of conflicting traffic streams,
the control treatment, and others. A primary factor is the value of "saturation queue discharge
headway", hsat, which applies to through vehicles that are not impeded by other conflicting
traffic streams. This value, itself, depends upon many factors including motorist behavior.
Formally, we can write,
ℎ ൌ𝑓ሺℎ௦௧,𝐹ଵ,𝐹ଶ,…ሻ
where:
hsat = Saturation discharge headway for through vehicles; seconds per vehicle
F1,F2 = The various known factors influencing hm
fm( ) = Complex function relating hm to the known (or estimated) values of hsat,
F1, F2, …
The estimation of hm for specified values of hsat, F1, F2, ... is undertaken within the DYNEV II
simulation model by a mathematical model2. The resulting values for hm always satisfy the
condition:
ℎ ℎ௦௧
That is, the turn‐movement‐specific discharge headways are always greater than, or equal to
the saturation discharge headway for through vehicles. These headways (or its inverse
equivalent, “saturation flow rate”), may be determined by observation or using the procedures
of the HCM 2016.
2Lieberman, E., "Determining Lateral Deployment of Traffic on an Approach to an Intersection", McShane, W. & Lieberman, E.,
"Service Rates of Mixed Traffic on the far Left Lane of an Approach". Both papers appear in Transportation Research Record 772,
1980. Lieberman, E., Xin, W., “Macroscopic Traffic Modeling For Large-Scale Evacuation Planning”, presented at the TRB 2012
Annual Meeting, January 22-26, 2012.
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The above discussion is necessarily brief given the scope of this Evacuation Time Estimate (ETE)
report and the complexity of the subject of intersection capacity. In fact, Chapters 19, 20 and 21
in the HCM 2016 address this topic. The factors, F1, F2, ..., influencing saturation flow rate are
identified in equation (19‐8) of the HCM 2016.
The traffic signals within the EMZ and Shadow Region are modeled using representative
phasing plans and phase durations obtained as part of the field data collection. Traffic
responsive signal installations allow the proportion of green time allocated (Pm) for each
approach to each intersection to be determined by the expected traffic volumes on each
approach during evacuation circumstances. The amount of green time (G) allocated is subject
to maximum and minimum phase duration constraints; 2 seconds of yellow time are indicated
for each signal phase and 1 second of all‐red time is assigned between signal phases, typically. If
a signal is pre‐timed, the yellow and all‐red times observed during the road survey are used. A
lost time (L) of 2.0 seconds is used for each signal phase in the analysis.
4.2 Capacity Estimation along Sections of Highway
The capacity of highway sections ‐‐ as distinct from approaches to intersections ‐‐ is a function
of roadway geometrics, traffic composition (e.g. percent heavy trucks and buses in the traffic
stream) and, of course, motorist behavior. There is a fundamental relationship which relates
service volume (i.e. the number of vehicles serviced within a uniform highway section in a given
time period) to traffic density. The top curve in Figure 4‐1 illustrates this relationship.
As indicated, there are two flow regimes: (1) Free Flow (left side of curve); and (2) Forced Flow
(right side). In the Free Flow regime, the traffic demand is fully serviced; the service volume
increases as demand volume and density increase, until the service volume attains its maximum
value, which is the capacity of the highway section. As traffic demand and the resulting highway
density increase beyond this "critical" value, the rate at which traffic can be serviced (i.e. the
service volume) can actually decline below capacity (“capacity drop”). Therefore, in order to
realistically represent traffic performance during congested conditions (i.e. when demand
exceeds capacity), it is necessary to estimate the service volume, VF, under congested
conditions.
The value of VF can be expressed as:
𝑉ி ൌ 𝑅 ൈ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦
where:
R = Reduction factor which is less than unity
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We have employed a value of R=0.90. The advisability of such a capacity reduction factor is
based upon empirical studies that identified a fall‐off in the service flow rate when congestion
occurs at “bottlenecks” or “choke points” on a freeway system. Zhang and Levinson3 describe a
research program that collected data from a computer‐based surveillance system (loop
detectors) installed on the Interstate Highway System, at 27 active bottlenecks in the twin cities
metro area in Minnesota over a 7‐week period. When flow breakdown occurs, queues are
formed which discharge at lower flow rates than the maximum capacity prior to observed
breakdown. These queue discharge flow (QDF) rates vary from one location to the next and
also vary by day of week and time of day based upon local circumstances. The cited reference
presents a mean QDF of 2,016 passenger cars per hour per lane (pcphpl). This figure compares
with the nominal capacity estimate of 2,250 pcphpl estimated for the ETE and indicated in
Appendix H for freeway links. The ratio of these two numbers is 0.896 which translates into a
capacity reduction factor of 0.90.
Since the principal objective of evacuation time estimate analyses is to develop a “realistic”
estimate of evacuation times, use of the representative value for this capacity reduction factor
(R=0.90) is justified. This factor is applied only when flow breaks down, as determined by the
simulation model.
Rural roads, like freeways, are classified as “uninterrupted flow” facilities. (This is in contrast
with urban street systems which have closely spaced signalized intersections and are classified
as “interrupted flow” facilities.) As such, traffic flow along rural roads is subject to the same
effects as freeways in the event traffic demand exceeds the nominal capacity, resulting in
queuing and lower QDF rates. As a practical matter, rural roads rarely break down at locations
away from intersections. Any breakdowns on rural roads are generally experienced at
intersections where other model logic applies, or at lane drops which reduce capacity there.
Therefore, the application of a factor of 0.90 is appropriate on rural roads, but rarely, if ever,
activated.
The estimated value of capacity is based primarily upon the type of facility and on roadway
geometrics. Sections of roadway with adverse geometrics are characterized by lower free‐flow
speeds and lane capacity. Exhibit 15‐46 in the Highway Capacity Manual was referenced to
estimate saturation flow rates. The impact of narrow lanes and shoulders on free‐flow speed
and on capacity is not material, particularly when flow is predominantly in one direction as is
the case during an evacuation.
The procedure used here was to estimate "section" capacity, VE, based on observations made
traveling over each section of the evacuation network, based on the posted speed limits and
travel behavior of other motorists and by reference to the 2016 HCM. The DYNEV II simulation
model determines for each highway section, represented as a network link, whether its
capacity would be limited by the "section‐specific" service volume, VE, or by the
intersection‐specific capacity. For each link, the model selects the lower value of capacity.
3Lei Zhang and David Levinson, “Some Properties of Flows at Freeway Bottlenecks,” Transportation Research Record 1883, 2004.
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4.3 Application to the City of Ashland Study Area
As part of the development of the link‐node analysis network for the study area, an estimate of
roadway capacity is required. The source material for the capacity estimates presented herein
is contained in:
2016 Highway Capacity Manual (HCM)
Transportation Research Board
National Research Council
Washington, D.C.
The highway system in the study area consists primarily of three categories of roads and, of
course, intersections:
Two‐Lane roads: Local, State
Multi‐Lane Highways (at‐grade)
Freeways
Each of these classifications will be discussed.
4.3.1 Two‐Lane Roads
Ref: HCM Chapter 15
Two lane roads comprise the majority of highways within the study area. The per‐lane capacity
of a two‐lane highway is estimated at 1,700 passenger cars per hour (pc/h). This estimate is
essentially independent of the directional distribution of traffic volume except that, for
extended distances, the two‐way capacity will not exceed 3,200 pc/h. The HCM procedures
then estimate LOS and Average Travel Speed. The DYNEV II simulation model accepts the
specified value of capacity as input and computes average speed based on the time‐varying
demand: capacity relations.
Based on the field survey and on expected traffic operations associated with evacuation
scenarios:
Most sections of two‐lane roads within the study area are classified as “Class I”, with
"level terrain"; some are “rolling terrain”.
“Class II” highways are mostly those within urban and suburban centers.
4.3.2 Multi‐Lane Highway
Ref: HCM Chapter 12
Exhibit 12‐8 of the HCM 2016 presents a set of curves that indicate a per‐lane capacity ranging
from approximately 1,900 to 2,300 pc/h, for free‐speeds of 45 to 70 mph, respectively. Based
on observation, the multi‐lane highways outside of urban areas within the study area service
traffic with free‐speeds in this range. The actual time‐varying speeds computed by the
simulation model reflect the demand and capacity relationship and the impact of control at
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intersections. A conservative estimate of per‐lane capacity of 1,900 pc/h is adopted for this
study for multi‐lane highways outside of urban areas, as shown in Appendix H.
4.3.3 Freeways
Ref: HCM Chapters 10, 12, 13, 14
Chapter 10 of the HCM 2016 describes a procedure for integrating the results obtained in
Chapters 12, 13 and 14, which compute capacity and LOS for freeway components. Chapter 10
also presents a discussion of simulation models. The DYNEV II simulation model automatically
performs this integration process.
Chapter 12 of the HCM 2016 presents procedures for estimating capacity and LOS for “Basic
Freeway Segments". Exhibit 12‐37 of the HCM 2016 presents capacity vs. free speed estimates,
which are provided below.
Free Speed (mph): 55 60 65 70+
Per‐Lane Capacity (pc/h): 2,250 2,300 2,350 2,400
The inputs to the simulation model are highway geometrics, free‐speeds and capacity based on
field observations. The simulation logic calculates actual time‐varying speeds based on demand:
capacity relationships. A conservative estimate of per‐lane capacity of 2,250 pc/h is adopted for
this study for freeways, as shown in Appendix H.
Chapter 13 of the HCM 2016 presents procedures for estimating capacity, speed, density and
LOS for freeway weaving sections. The simulation model contains logic that relates speed to
demand volume: capacity ratio. The value of capacity obtained from the computational
procedures detailed in Chapter 13 depends on the "Type" and geometrics of the weaving
segment and on the "Volume Ratio" (ratio of weaving volume to total volume).
Chapter 14 of the HCM 2016 presents procedures for estimating capacities of ramps and of
"merge" areas. There are three significant factors to the determination of capacity of a ramp‐
freeway junction: The capacity of the freeway immediately downstream of an on‐ramp or
immediately upstream of an off‐ramp; the capacity of the ramp roadway; and the maximum
flow rate entering the ramp influence area. In most cases, the freeway capacity is the
controlling factor. Values of this merge area capacity are presented in Exhibit 14‐10 of the HCM
2016 and depend on the number of freeway lanes and on the freeway free speed. Ramp
capacity is presented in Exhibit 14‐12 and is a function of the ramp’s FFS. The DYNEV II
simulation model logic simulates the merging operations of the ramp and freeway traffic in
accord with the procedures in Chapter 14 of the HCM 2016. If congestion results from an
excess of demand relative to capacity, then the model allocates service appropriately to the
two entering traffic streams and produces LOS F conditions (The HCM does not address LOS F
explicitly).
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4.3.4 Intersections
Ref: HCM Chapters 19, 20, 21, 22
Procedures for estimating capacity and LOS for approaches to intersections are presented in
Chapter 19 (signalized intersections), Chapters 20, 21 (un‐signalized intersections) and Chapter
22 (roundabouts). The complexity of these computations is indicated by the aggregate length
of these chapters. The DYNEV II simulation logic is likewise complex.
The simulation model explicitly models intersections: Stop/yield controlled intersections (both
2‐way and all‐way) and traffic signal controlled intersections. Where intersections are
controlled by fixed time controllers, traffic signal timings are set to reflect average (non‐
evacuation) traffic conditions. Actuated traffic signal settings respond to the time‐varying
demands of evacuation traffic to adjust the relative capacities of the competing intersection
approaches.
The model is also capable of modeling the presence of manned traffic control. At specific
locations where it is advisable or where existing plans call for overriding existing traffic control
to implement manned control, the model will use actuated signal timings that reflect the
presence of traffic guides. At locations where a special traffic control strategy (continuous left‐
turns, contra‐flow lanes) is used, the strategy is modeled explicitly. Where applicable, the
location and type of traffic control for nodes in the evacuation network are noted in Appendix
H.
4.4 Simulation and Capacity Estimation
Chapter 6 of the HCM is entitled, “HCM and Alternative Analysis Tools.” The chapter discusses
the use of alternative tools such as simulation modeling to evaluate the operational
performance of highway networks. Among the reasons cited in Chapter 6 to consider using
simulation as an alternative analysis tool is:
“The system under study involves a group of different facilities or travel modes with
mutual interactions involving several HCM chapters. Alternative tools are able to analyze
these facilities as a single system.”
This statement succinctly describes the analyses required to determine traffic operations across
an area encompassing a study area operating under evacuation conditions. The model utilized
for this study, DYNEV II, is further described in Appendix C. It is essential to recognize that
simulation models do not replicate the methodology and procedures of the HCM – they replace
these procedures by describing the complex interactions of traffic flow and computing
Measures of Effectiveness (MOE) detailing the operational performance of traffic over time and
by location. The DYNEV II simulation model includes some HCM 2016 procedures only for the
purpose of estimating capacity.
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All simulation models must be calibrated properly with field observations that quantify the
performance parameters applicable to the analysis network. Two of the most important of
these are: (1) FFS; and (2) saturation headway, hsat. The first of these is estimated by direct
observation during the road survey; the second is estimated using the concepts of the HCM
2016, as described earlier. These parameters are listed in Appendix H, for each network link.
It is important to note that simulation represents a mathematical representation of an assumed
set of conditions using the best available knowledge and understanding of traffic flow and
available inputs. Simulation should not be assumed to be a prediction of what will happen
under any event because a real evacuation can be impacted by an infinite number of things –
many of which will differ from these test cases – and many others cannot be taken into account
with the tools available.
4.5 Boundary Conditions
As illustrated in Figure 1‐2 and in Appendix H, the link‐node analysis network used for this study
is finite. The analysis network extends well beyond the EMZ in order to model intersections
with other major population areas and evacuation routes beyond the study area. However, the
network does have an end at the destination (exit) nodes as discussed in Appendix C. Beyond
these destination nodes, there may be signalized intersections or merge points that impact the
capacity of the evacuation routes leaving the study area. Rather than neglect these “boundary
conditions,” this study assumes a 25% reduction in capacity on two‐lane roads (Section 4.3.1
above) and multi‐lane highways (Section 4.3.2 above). There is no reduction in capacity for
freeways due to boundary conditions. The 25% reduction in capacity is based on the prevalence
of actuated traffic signals in the study area and the fact that the evacuating traffic volume will
be more significant than the competing traffic volume at any downstream signalized
intersections, thereby warranting a more significant percentage (75% in this case) of the signal
green time.
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Figure 4‐1. Fundamental Diagrams
Capacity Drop
Qs
f s
Volume, vph
Qmax
Speed, mph
R vc
k k j opt k
Flow Regimes
Density, vpm
Density, vpm
R Qmax
k
vf
Free Forced
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5 ESTIMATION OF TRIP GENERATION TIME
It is general practice for planners to estimate the distributions of elapsed times associated with
mobilization activities undertaken by the public to prepare for the evacuation trip. The elapsed
time associated with each activity is represented as a statistical distribution reflecting
differences between members of the public. The quantification of these activity‐based
distributions relies largely on the results of the demographic survey. We define the sum of
these distributions of elapsed times as the Trip Generation Time Distribution. This section
documents how the trip generation time distributions were estimated.
5.1 Background
In general, during a wildfire emergency, priorities are given to life safety, preservation of
property and resource conservation. To ensure life safety, depending on the severity, wind
speed and direction of the wildfire, emergency officials may issue warnings that include
evacuation.
As a Planning Basis, we will adopt a conservative posture, a rapidly escalating wildfire situation,
wherein evacuation is required, ordered promptly and no early protective actions have been
implemented when calculating the Trip Generation Time. In these analyses, we have assumed:
1. The advisory to evacuate will be announced coincident with local emergency alerts (e.g.
emergency alert systems (EAS) broadcasts, sirens, social media, local news, door‐to‐
door and with alike communication systems).
2. Mobilization of the general population will commence within 15 minutes after
emergency alerts.
3. ETE are measured relative to the advisory to evacuate.
We emphasize that the adoption of this planning basis is not a representation that these events
will occur within the indicated time frame. Rather, these assumptions are necessary in order to:
1. Establish a temporal framework for estimating the Trip Generation distribution
2. Identify temporal points of reference that uniquely define "Clear Time" and ETE.
The notification process consists of two events:
1. Transmitting information using the alert and notification systems mentioned above.
2. Receiving and correctly interpreting the information that is transmitted.
The population within the Emergency Management Zone (EMZ) is dispersed over an area of
approximately 6.7 square miles and is engaged in a wide variety of activities. It must be
anticipated that some time will elapse between the transmission and receipt of the information
advising the public of an event.
The amount of elapsed time will vary from one individual to the next depending on where that
person is, what that person is doing, and related factors. Furthermore, some persons who will
be directly involved with the evacuation process may be outside the EMZ at the time the
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emergency is declared. These people may be commuters, shoppers and other travelers who
reside within the EMZ and who will return to join the other household members upon receiving
notification of an emergency.
As indicated in Section 2.13 of NUREG/CR‐6863, the estimated elapsed times for the receipt of
notification can be expressed as a distribution reflecting the different notification times for
different people within, and outside, the EMZ. By using time distributions, it is also possible to
distinguish between different population groups and different day‐of‐week and time‐of‐day
scenarios, so that accurate ETE may be computed.
For example, people at home or at work within the EMZ might be notified by wireless
emergency alerts, television and/or radio (if available). Those well outside the EMZ might be
notified by word‐of‐mouth, with potentially longer time lags. Furthermore, the spatial
distribution of the EMZ population will differ with time of day ‐ families will be united in the
evenings but dispersed during the day. In this respect, weekends will differ from weekdays.
As indicated in Section 4.1 of NUREG/CR‐7002, the information required to compute trip
generation times is typically obtained from a demographic survey of residents. Such a survey
was conducted for this study. Appendix F presents the survey sampling results, survey
instrument, and raw survey results. The remaining discussion will focus on the application of
the trip generation data obtained from the demographic survey to the development of the ETE
documented in this report.
5.2 Fundamental Considerations
The environment leading up to the time that people begin their evacuation trips consists of a
sequence of events and activities. Each event (other than the first) occurs at an instant in time
and is the outcome of an activity.
Activities are undertaken over a period of time. Activities may be in "series" (i.e. to undertake
an activity implies the completion of all preceding events) or may be in parallel (two or more
activities may take place over the same period of time). Activities conducted in series are
functionally dependent on the completion of prior activities; activities conducted in parallel are
functionally independent of one another. The relevant events associated with the public's
preparation for evacuation are:
Event Number Event Description
1 Notification
2 Awareness of Situation
3 Depart Work
4 Arrive Home
5 Depart on Evacuation Trip
Associated with each sequence of events are one or more activities, as outlined in Table 5‐1.
These relationships are shown graphically in Figure 5‐1.
An Event is a ‘state’ that exists at a point in time (e.g., depart work, arrive home)
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An Activity is a ‘process’ that takes place over some elapsed time (e.g., prepare to leave
work, travel home)
As such, a completed Activity changes the ‘state’ of an individual (e.g. the activity, ‘travel home’
changes the state from ‘depart work’ to ‘arrive home’). Therefore, an Activity can be described as
an ‘Event Sequence’; the elapsed times to perform an event sequence vary from one person to the
next and are described as statistical distributions on the following pages.
An employee who lives outside of the EMZ will follow sequence (c) of Figure 5‐1. A household
within the EMZ that has one or more commuters at work and will await their return before
beginning the evacuation trip will follow the first sequence of Figure 5‐1(a). A household within
the EMZ that has no commuters at work, or that will not await the return of any commuters,
will follow the second sequence of Figure 5‐1(a), regardless of day of week or time of day.
Households with no commuters on weekends or in the evening/night‐time will follow the
applicable sequence in Figure 5‐1(b). Tourists will always follow one of the sequences of Figure
5‐1(b). Some tourists away from their residence could elect to evacuate immediately without
returning to the residence, as indicated in the second sequence.
It is seen from Figure 5‐1, that the Trip Generation time (i.e. the total elapsed time from Event 1
to Event 5) depends on the scenario and will vary from one household to the next.
Furthermore, Event 5 depends, in a complicated way, on the time distributions of all activities
preceding that event. That is, to estimate the time distribution of Event 5, we must obtain
estimates of the time distributions of all preceding events. For this study, we adopt the
conservative posture that all activities will occur in sequence.
In some cases, assuming certain events occur strictly sequential (for instance, commuter
returning home before beginning preparation to leave) can result in rather conservative (that is,
longer) estimates of mobilization times. It is reasonable to expect that at least some parts of
these events will overlap for many households, but that assumption is not made in this study.
5.3 Estimated Time Distributions of Activities Preceding Event 5
The time distribution of an event is obtained by "summing" the time distributions of all prior
contributing activities. (This "summing" process is quite different than an algebraic sum since it
is performed on distributions – not scalar numbers).
Time Distribution No. 1, Notification Process: Activity 1 2
A demographic survey of Ashland residents was conducted to study evacuation behavior of the
population within the EMZ. The survey results were used to create the notification time
distribution. The survey asked specific questions about notifying neighbors and friends during
an emergency using various methods like phone calls, text messages, social media, and in
person conversation. Since the survey was statistically significant at the 99% confidence level, it
can be assumed that the population within the EMZ will behave similarly to the survey
respondents.
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The City of Ashland uses emergency alert systems such as NIXLE and Citizen Alert to push
notifications to the population opted‐in to the service.
Given the presence of the existing emergency alert systems and the responses to the
demographic survey regarding notification of friends and neighbors, it was assumed that about
63% of the EMZ population can be notified within 5 minutes of an emergency, about 95% of the
EMZ population can be notified within 15 minutes, and 100% of the EMZ population can be
notified within 45 minutes. The distribution of Activity 1 → 2 shown in Table 5‐2 reflects data
obtained by the demographic survey and the above assumptions.
Given the uncertainty in some critical assumptions, several sensitivity studies were conducted
as part of this work effort to determine the elasticity of the evacuation time estimates to those
assumptions, see Appendix J.
Distribution No. 2, Prepare to Leave Work: Activity 2 3
It is reasonable to expect that the vast majority of business enterprises within the EMZ will
elect to shut down following notification and most employees would leave work
quickly. Commuters, who work outside the EMZ could, in all probability, also leave quickly
since facilities outside the EMZ would remain open and other personnel would
remain. Personnel or farmers responsible for equipment/livestock would require additional
time to secure their facility. The distribution of Activity 2 → 3 shown in Table 5‐3 reflects data
obtained by the demographic survey. This distribution is also applicable for residents to leave
stores, restaurants, parks and other locations within the EMZ. This distribution is plotted in
Figure 5‐2.
Distribution No. 3, Travel Home: Activity 3 4
These data are provided directly by households that responded to the demographic survey. This
distribution is plotted in Figure 5‐2 and listed in Table 5‐4.
Distribution No. 4, Prepare to Leave Home: Activity 2, 4 5
These data are provided directly by households that responded to the demographic survey. This
distribution is plotted in Figure 5‐2 and listed in Table 5‐5.
5.4 Calculation of Trip Generation Time Distribution
The time distributions for each of the mobilization activities presented herein must be
combined to form the appropriate Trip Generation Distributions. As discussed above, this study
assumes that the stated events take place in sequence such that all preceding events must be
completed before the current event can occur. For example, if a household awaits the return
of a commuter, the work‐to‐home trip (Activity 3 4) must precede Activity 4 5.
To calculate the time distribution of an event that is dependent on two sequential activities, it is
necessary to “sum” the distributions associated with these prior activities. The distribution
summing algorithm is applied repeatedly as shown to form the required distribution. As an
outcome of this procedure, new time distributions are formed; we assign “letter” designations
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to these intermediate distributions to describe the procedure. Table 5‐6 presents the summing
procedure to arrive at each designated distribution.
Table 5‐7 presents a description of each of the final trip generation distributions achieved after the
summing process is completed.
5.4.1 Statistical Outliers
As already mentioned, some portion of the survey respondents answer “Decline to State” to some
questions or choose to not respond to a question. The mobilization activity distributions are based
upon actual responses. But it is the nature of surveys that a few numeric responses are
inconsistent with the overall pattern of results. An example would be a case in which for 500
responses, almost all of them estimate less than two hours for a given answer, but 3 say “four
hours” and 4 say “six or more hours”.
These “outliers” must be considered: are they valid responses, or so atypical that they should be
dropped from the sample?
In assessing outliers, there are three alternates to consider:
1) Some responses with very long times may be valid, but reflect the reality that the
respondent really needs to be classified in a different population subgroup, based upon
special needs;
2) Other responses may be unrealistic (6 hours to return home from commuting distance,
or 2 days to prepare the home for departure);
3) Some high values are representative and plausible, and one must not cut them as part
of the consideration of outliers.
The issue is how to make the decision that a given response or set of responses are to be
considered “outliers” for the component mobilization activities, using a method that objectively
quantifies the process.
There is considerable statistical literature on the identification and treatment of outliers singly or
in groups, much of which assumes the data is normally distributed and some of which uses non‐
parametric methods to avoid that assumption. The literature cites that limited work has been
done directly on outliers in sample survey responses.
In establishing the overall mobilization time/trip generation distributions, the following principles
are used:
1) It is recognized that the overall trip generation distributions are conservative estimates,
because they assume a household will do the mobilization activities sequentially, with no
overlap of activities;
2) The individual mobilization activities (receive notification, prepare to leave work, travel
home, prepare home) are reviewed for outliers, and then the overall trip generation
distributions are created (see Figure 5‐1, Table 5‐6, Table 5‐7);
3) Outliers can be eliminated either because the response reflects a special population (e.g.
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special needs, transit dependent) or lack of realism, because the purpose is to estimate trip
generation patterns for personal vehicles;
4) To eliminate outliers,
a) the mean and standard deviation of the specific activity are estimated from the
responses,
b) the median of the same data is estimated, with its position relative to the mean
noted,
c) the histogram of the data is inspected, and
d) all values greater than 3.5 standard deviations are flagged for attention, taking
special note of whether there are gaps (categories with zero entries) in the
histogram display.
In general, only flagged values more than 4 standard deviations1 from the mean are
allowed to be considered outliers, with gaps in the histogram expected.
When flagged values are classified as outliers and dropped, steps “a” to “d” are repeated.
5) As a practical matter, even with outliers eliminated by the above, the resultant histogram,
viewed as a cumulative distribution, is not a normal distribution. A typical situation that
results is shown below in Figure 5‐3.
6) In particular, the cumulative distribution differs from the normal distribution in two key
aspects, both very important in loading a network to estimate evacuation times:
Most of the real data is to the left of the “normal” curve above, indicating that the
network loads faster for the first 80‐85% of the vehicles, potentially causing more (and
earlier) congestion than otherwise modeled;
The last 10‐15% of the real data “tails off” slower than the comparable “normal” curve,
indicating that there is significant traffic still loading at later times.
Because these two features are important to preserve, it is the histogram of the data that
is used to describe the mobilization activities, not a “normal” curve fit to the data. One
could consider other distributions, but using the shape of the actual data curve is
unambiguous and preserves these important features;
7) With the mobilization activities each modeled according to Steps 1‐6, including preserving
the features cited in Step 6, the overall (or total) mobilization times are constructed.
This is done by using the data sets and distributions under different scenarios (e.g. commuter
returning, no commuter returning in each). In general, these are additive, using weighting based
upon the probability distributions of each element; Figure 5‐4 presents the combined trip
generation distributions designated A, C, and D. These distributions are presented on the same
time scale. (As discussed earlier, the use of strictly additive activities is a conservative approach,
because it makes all activities sequential – preparation for departure follows the return of the
1 This rule was followed for all truncation analyses except the time distribution for notification. The truncation for the notification
distribution used 8 standard deviations as the responses that fell within 8 standard deviations did not appear to be statistical outliers.
Only those data points that fell beyond 8 standard deviations from the mean were considered outliers. Basically, more data points
were included in the distribution (rather than eliminated as outliers).
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commuter. In practice, it is reasonable that some of these activities are done in parallel, at least to
some extent – for instance, preparation to depart begins by a household member at home while
the commuter is still on the road.)
The mobilization distributions that result is used in their tabular/graphical form as direct inputs to
later computations that lead to the ETE.
The DYNEV II simulation model is designed to accept varying rates of vehicle trip generation for
each origin centroid, expressed in the form of histograms. These histograms, which represent
Distributions A, C, and D, properly displaced with respect to one another, are tabulated in Table 5‐
8 (Distribution B, Arrive Home, omitted for clarity).
The final time period (15) is 600 minutes long. This time period is added to allow the analysis
network to clear, in the event congestion persists beyond the trip generation period. Note that
there are no trips generated during this final time period.
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Table 5‐1. Event Sequence for Evacuation Activities
Event Sequence Activity Distribution
1 → 2 Receive Notification 1
2 → 3 Prepare to Leave Work 2
2,3 → 4 Travel Home 3
2,4 → 5 Prepare to Leave to Evacuate 4
Table 5‐2. Time Distribution for Notifying the Public
Elapsed Time
(Minutes)
Cumulative
Percent
Notified
0 0.0%
5 62.8%
10 84.5%
15 94.5%
20 97.3%
25 98.0%
30 99.2%
35 99.8%
40 99.9%
45 100.0%
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Table 5‐3. Time Distribution for Employees to Prepare to Leave Work/College
NOTE: The survey data was normalized to distribute the "Don't know" response. That is, the sample was reduced in
size to include only those households who responded to this question. The underlying assumption is that the
distribution of this activity for the “Don’t know” responders, if the event takes place, would be the same as those
responders who provided estimates.
Elapsed Time
(Minutes)
Cumulative
Percent
Employees
Leaving Work
0 0%
5 35%
10 61%
15 77%
20 84%
25 87%
30 92%
35 94%
40 94%
45 96%
50 96%
55 97%
60 100%
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Table 5‐4. Time Distribution for Commuters to Travel Home
Elapsed Time
(Minutes)
Cumulative
Percent
Returning Home
0 0%
5 16%
10 41%
15 56%
20 69%
25 82%
30 92%
35 95%
40 97%
45 98%
50 99%
55 99%
60 100%
NOTE: The survey data was normalized to distribute the “Don’t know” response.
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Table 5‐5. Time Distribution for Population to Prepare to Evacuate
Elapsed Time
(Minutes)
Cumulative
Percent Ready to
Evacuate
0 0%
15 7%
30 38%
45 56%
60 76%
75 84%
90 87%
105 89%
120 93%
135 97%
150 98%
165 98%
180 99%
195 100%
NOTE: The survey data was normalized to distribute the "Don't know" response.
Table 5‐6. Mapping Distributions to Events
Apply “Summing” Algorithm To: Distribution Obtained Event Defined
Distributions 1 and 2 Distribution A Event 3
Distributions A and 3 Distribution B Event 4
Distributions B and 4 Distribution C Event 5
Distributions 1 and 4 Distribution D Event 5
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Table 5‐7. Description of the Distributions
Distribution Description
A
Time distribution of commuters departing place of work (Event 3). Also applies
to employees who work within the EMZ who live outside, and to Tourists within
the EMZ.
B Time distribution of commuters arriving home (Event 4).
C Time distribution of residents with commuters who return home, leaving home
to begin the evacuation trip (Event 5).
D Time distribution of residents without commuters returning home, leaving home
to begin the evacuation trip (Event 5).
Table 5‐8. Trip Generation Histograms for the EMZ Population
Time
Period
Duration
(Min)
Percent of Total Trips Generated Within Indicated Time Period
Employees
(Distribution A)
Tourists
(Distribution B)
Residents with
Commuters
(Distribution C)
Residents
Without
Commuters
(Distribution D)
1 15 46% 46% 0% 3%
2 15 36% 36% 1% 19%
3 15 11% 11% 7% 24%
4 15 3% 3% 16% 20%
5 15 4% 4% 19% 13%
6 15 0% 0% 18% 6%
7 15 0% 0% 13% 3%
8 15 0% 0% 8% 3%
9 15 0% 0% 4% 4%
10 15 0% 0% 4% 2%
11 15 0% 0% 4% 1%
12 15 0% 0% 2% 0%
13 30 0% 0% 2% 2%
14 30 0% 0% 2% 0%
15 600 0% 0% 0% 0%
NOTE:
Shadow vehicles are loaded onto the analysis network (Figure 1‐2) using Distribution C.
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Figure 5‐1. Events and Activities Preceding the Evacuation Trip
households who do not
Tourists evacuate directly
Households wait
for Commuters 1
Residents
1 2 3 4 5
EVENTS
1. Notification 2. Aware of situation 3. Depart work 4. Arrive home 5. Depart on evacuation trip
(a) Ignition occurs during midweek, at midday; year round
Households without
Commuters and
wait for Commuters
Residents
1 2 5
Residents,
Tourists at
Residence
1 2 5
(b) Ignition occurs during weekend or during the evening2
(c) Employees who live outside of the EMZ
Residents,
Tourists
away from
Residence
1 2 4 5
1 2 3, 5
Return to residence,
then evacuate
Residents at home;
1 Applies for evening and weekends also if commuters are at work. 2 Applies throughout the year for tourists.
#
ACTIVITIES
1 2 Receive Notification
2 3 Prepare to Leave Work
2, 3 4 Travel Home
2, 4 5 Prepare to Leave to Evacuate
Activities Consume Time
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Figure 5‐2. Evacuation Mobilization Activities
0%
20%
40%
60%
80%
100%
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Elapsed Time from Start of Mobilization Activity (min)
Mobilization Activities
Notification
Prepare to Leave Work
Travel Home
Prepare Home
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Figure 5‐3. Comparison of Data Distribution and Normal Distribution
0.0%
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Cumulative Data Cumulative Normal
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Figure 5‐4. Comparison of Trip Generation Distributions
0
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0 60 120 180 240
Pe
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Elapsed Time from Evacuation Advisory (min)
Trip Generation Distributions
Employees/Tourists Residents with Commuters Residents with no Commuters
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6 EVACUATION CASES
This section discusses the spatial and temporal variations in evacuation situations. The regions
outlined in the study were created based on various geometric areas that would be evacuated
in response to a wildfire emergency. The scenarios outlined in the study were created based on
the various temporal changes that affect the number of vehicles evacuating during a wildfire
emergency. This section provides an overview of all the possible evacuation cases that were
studied. An evacuation “case” defines a combination of Evacuation Region and Evacuation
Scenario. For this specific study, the definitions of “Region” and “Scenario” are as follows:
Region A grouping of evacuating EMZ, or an individual EMZ, that must be evacuated in
response to a wildfire emergency.
Scenario A combination of circumstances, including time of day, day of week, and
season. Scenarios define the number of people in each of the affected
population groups and their respective mobilization time distributions.
A total of 18 Regions were defined which encompass all the groupings of EMZ considered.
These Regions are defined in Table 6‐1 by showing which EMZ evacuates for each Region. EMZs
marked with a red “X” evacuate for that given Region. The EMZ boundaries are identified in
Figure 6‐1. The EMZ boundaries were provided by the City of Ashland.
Regions R01 through R10 represent evacuations of each individual EMZ by itself. Regions R11
through R17 are evacuations of combinations of EMZ based on the origin of a potential wildfire
and prevailing winds. Lastly, Region R18 is the evacuation of all EMZs at once.
A total of 6 Scenarios were evaluated for all Regions. Thus, there are a total of 108 (18 x 6 =
108) evacuation cases. Table 6‐2 is a description of all Scenarios.
Each combination of Region and Scenario implies a specific population to be evacuated. The
population group and the vehicle estimates presented in Section 3 and in Appendix E are peak
values. These peak values are adjusted depending on the scenario and region being considered,
using Scenario and Region‐specific percentages, such that the average population is considered
for each evacuation case. The Scenario percentages are presented in Table 6‐3, while the
Region percentages are provided in Table G‐1.
Table 6‐4 presents the vehicle counts for each scenario for an evacuation of Region R18 – all
EMZs. Based on the scenario percentages in Table 6‐3. The percentages presented in Table 6‐3
were determined as follows:
The number of residents with commuters during the week (when workforce is at its peak) is
equal to the product of 36% (the number of households with at least one commuter) and 39%
(the number of households with a commuter that would await the return of the commuter
prior to evacuating) – 14 percent. See assumption 16 in Section 2.3. It is estimated for weekend
and evening scenarios that 10% of households with returning commuters will have a commuter
at work during those times.
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Employment is assumed to be at its peak during the fall, midweek, midday scenarios.
Employment is reduced slightly (96%) for summer, midweek, midday scenarios. This is based on
the estimation that 50% of the employees commuting into the EMZ will be on vacation for a
week during the approximate 12 weeks of summer. It is further estimated that those taking
vacation will be uniformly dispersed throughout the summer with approximately 4% of
employees vacationing each week. It is further estimated that only 10% of the employees are
working in the evenings and during the weekends.
Tourist activity is estimated to be at its peak (100%) during evenings due to the high number of
lodging facilities in the area. During the daytime, summer weekdays and weekends have slightly
more tourists (55% and 70%, respectively) than during fall weekdays and weekend (50% and
65%, respectively) since the golf course and hiking trails peak during the summer season and on
weekends.
As noted in the shadow footnote to Table 6‐3, the shadow percentages are computed using a
base of 6% (see assumption 3 in Section 2.3); to include the employees within the shadow
region who may choose to evacuate, the voluntary evacuation is multiplied by a scenario‐
specific proportion of employees to permanent residents in the shadow region. For example,
using the values provided in Table 6‐4 for Scenario 1, the shadow percentage is computed as
follows:
6%ൈ ൬1 2,086
1,921 11,745൰ ൌ 7%
As discussed in Section 7, schools are in session during the fall season, midweek, midday and
100% of buses will be needed under those circumstances. In addition, 100% of on and off
campus students at Southern Oregon University are assumed to be present in the fall, with only
the on campus students being present on weekends and evenings. It is estimated that summer
school/commuter college enrollment is approximately 10% of enrollment during the regular
school year for summer scenarios. School is not in session during weekends and evenings, thus
no buses for school children are needed under those circumstances.
Transit buses for the transit‐dependent population and medical patients are set to 100% for all
scenarios as it is assumed that the transit‐dependent population and medical patients are
present in the EMZ for all scenarios.
External traffic is estimated to be reduced by 60% during evening scenarios and is 100% for all
other scenarios.
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Table 6‐1. Description of Evacuation Regions
Region
Emergency Management Zone (EMZ)
Description 1 2 3 4 5 6 7 8 9 10
R01 EMZ1 X
R02 EMZ2 X
R03 EMZ3 X
R04 EMZ4 X
R05 EMZ5 X
R06 EMZ6 X
R07 EMZ7 X
R08 EMZ8 X
R09 EMZ9 X
R10 EMZ10 X
R11
Western
Ashland ‐ EMZ1,
EMZ2, EMZ3,
EMZ4
X X X X
R12
Eastern Ashland
‐ EMZ5, EMZ6,
EMZ7, EMZ8,
EMZ9, EMZ10
X X X X X X
R13
Northern
Ashland ‐ EMZ1,
EMZ2, EMZ10
X X X
R14
Central Ashland
‐ EMZ3, EMZ7,
EMZ8, EMZ9
X X X X
R15
Southern
Ashland ‐ EMZ4,
EMZ5, EMZ6
X X X
R16
Northern and
Central Ashland
‐ EMZ1, EMZ2,
EMZ3, EMZ7,
EMZ8, EMZ9,
EMZ10
X X X X X X X
R17
Southern and
Central Ashland
‐ EMZ3, EMZ4,
EMZ5, EMZ6,
EMZ7, EMZ8,
EMZ9
X X X X X X X
R18 All EMZ X X X X X X X X X X
EMZ(s) Shelter‐in‐Place EMZ(s) Evacuate
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Table 6‐2. Evacuation Scenario Definitions
Scenario Season Day of Week Time of Day
1 Summer Midweek Midday
2 Summer Weekend Midday
3 Summer Midweek, Weekend Evening
4 Fall Midweek Midday
5 Fall Weekend Midday
6 Fall Midweek, Weekend Evening
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Table 6‐3. Percent of Population Groups Evacuating for Various Scenarios
Scenario
Households
With
Returning
Commuters
Households
Without
Returning
Commuters Employees Tourists Shadow
Medical
Vehicles
On Campus
Vehicles
Off
Campus
Vehicles
School/College
Buses
Transit
Buses
External
Through
Traffic
1 14% 86% 96% 55% 7% 100% 10% 10% 10% 100% 100%
2 1% 99% 10% 70% 6% 100% 0% 0% 0% 100% 100%
3 1% 99% 10% 100% 6% 100% 0% 0% 0% 100% 40%
4 14% 86% 100% 50% 7% 100% 100% 100% 100% 100% 100%
5 1% 99% 10% 65% 6% 100% 100% 0% 0% 100% 100%
6 1% 99% 10% 100% 6% 100% 100% 0% 0% 100% 40%
Resident Households with Commuters ....... Households of EMZ residents who await the return of commuters prior to beginning the evacuation trip.
Resident Households with No Commuters .. Households of EMZ residents who do not have commuters or will not await the return of commuters prior to beginning the evacuation trip.
Employees ................................................. Employees who live outside the EMZ but work within.
Tourists ..................................................... People who are in the EMZ at the time of an event for recreational or other (non‐employment) purposes.
Shadow ..................................................... Residents and employees in the shadow region (outside of the EMZ) who will spontaneously decide to relocate during the evacuation. The basis for the values shown is a 6%
relocation of shadow residents along with a proportional percentage of shadow employees.
On Campus Vehicles ................................... Students who reside on campus within the EMZ that will evacuate using a private vehicle.
Off Campus Vehicles .................................. Students who reside off campus within the EMZ that will evacuate using a private vehicle.
Medical, School and Transit Buses .............. Vehicle‐equivalents present on the road during evacuation servicing medical facilities, schools and transit‐dependent people (1 bus is equivalent to 2 passenger vehicles).
External Through Traffic ............................. Traffic on interstates and major arterial roads at the start of the evacuation. This traffic is stopped by access control approximately 2 hours after the evacuation begins.
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Table 6‐4. Vehicle Estimates by Scenario
Scenarios
Residents
with
Commuters
Residents
without
Commuters
Employees Tourists Shadow Medical
Vehicles
On
Campus
Vehicles
Off
Campus
Vehicles
School
Buses
Transit
Buses
External
Traffic
Total
Scenario
Vehicles
1 1,921 11,745 2,086 1,239 449 56 51 392 17 8 8,412 26,376
2 192 13,473 217 1,576 396 56 ‐ ‐ ‐ 8 8,412 24,330
3 192 13,473 217 2,252 396 56 ‐ ‐ ‐ 8 3,365 19,959
4 1,921 11,745 2,173 1,126 451 56 510 3,915 172 8 8,412 30,489
5 192 13,473 217 1,464 396 56 ‐ ‐ ‐ 8 8,412 24,218
6 192 13,473 217 2,252 396 56 ‐ ‐ ‐ 8 3,365 19,959
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Figure 6‐1. EMZ Boundaries
City of Ashland 7‐1 KLD Engineering, P.C.
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7 GENERAL POPULATION EVACUATION TIME ESTIMATES (ETE)
This section presents the ETE results of the computer analyses using the DYNEV II System
described in Appendices B, C and D. These results cover 18 Evacuation Regions and the 6
Evacuation Scenarios discussed in Section 6.
The ETE for all Evacuation Cases are presented in Table 7‐1 and Table 7‐2. These tables present
the estimated times to clear the indicated population percentages from the Evacuation Regions
for all Evacuation Scenarios. Table 6‐1 and Table G‐1 defines the Evacuation Regions considered.
The tabulated values of ETE are obtained from the DYNEV II System outputs which are generated
at 5‐minute intervals.
7.1 Voluntary Evacuation and Shadow Evacuation
“Voluntary evacuees” are people within the EMZ for which an Advisory to Evacuate has not been
issued, yet who elect to evacuate. “Shadow evacuation” is the voluntary outward movement of
some people from the Shadow Region for whom no evacuation order has been issued. Both
voluntary and shadow evacuations are assumed to take place over the same time frame as the
evacuation from within the impacted Evacuation Region.
Within the EMZ, 6 percent of permanent residents located outside of the evacuation region who
are not advised to evacuate, are assumed to elect to evacuate. Similarly, it is assumed that 6
percent of those people in the Shadow Region will choose to leave the area.
Figure 7‐1 presents the area identified as the Shadow Region. The Shadow Region was defined
as the area beyond the EMZ including the City of Talent. The Shadow Region is bounded by the
northern city limit of Talent to the north, the ridge line and the United States Forest Service
(USFS) border to the west, a horizontal line connecting the USFS border to Emigrant Lake to the
south, and the ridge line to the east. The population and number of evacuating vehicles in the
Shadow Region were estimated using the same methodology that was used for permanent
residents within the EMZ (see Section 3.1). As discussed in Section 3.2, it is estimated that a total
of 10,099 people reside in the Shadow Region; 6 percent of them would evacuate. See Table 6‐
4 for the number of evacuating vehicles from the Shadow Region.
Traffic generated within this Shadow Region including external‐external traffic, traveling away
from the wildfire, has the potential for impeding evacuating vehicles from within the Evacuation
Region. All ETE calculations include this shadow traffic movement.
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7.2 Patterns of Traffic Congestion during Evacuation
Figure 7‐2 through Figure 7‐7 illustrate the patterns of traffic congestion that arise for the case
when all ten EMZs (Region R18) are advised to evacuate during a summer, midweek, midday
period (Scenario 4).
Traffic congestion, as the term is used here, is defined as Level of Service (LOS) F. LOS F is defined
as follows (HCM 2016, page 5‐5):
The HCM uses LOS F to define operations that have either broken down (i.e., demand
exceeds capacity) or have reached a point that most users would consider unsatisfactory,
as described by a specified service measure value (or combination of service measure
values). However, analysts may be interested in knowing just how bad the LOS F condition
is, particularly for planning applications where different alternatives may be compared.
Several measures are available for describing individually, or in combination, the severity
of a LOS F condition:
• Demand‐to‐capacity ratios describe the extent to which demand exceeds
capacity during the analysis period (e.g., by 1%, 15%).
• Duration of LOS F describes how long the condition persists (e.g., 15 min, 1 h, 3
h).
• Spatial extent measures describe the areas affected by LOS F conditions. They
include measures such as the back of queue and the identification of the specific
intersection approaches or system elements experiencing LOS F conditions.
All highway "links" which experience LOS F are delineated in these figures by a thick red line; all
others are lightly indicated. Congestion develops around concentrations of population and traffic
bottlenecks.
Figure 7‐2 displays the congestion patterns in the study area at just 30 minutes after the advisory
to evacuate. Severe congestion has already developed on major evacuation routes, such as
Siskiyou Blvd (OR‐99) and Ashland St (OR‐66), and many of the local roadways within the City of
Ashland. The on ramps to I‐5 have limited capacity and, as a result, meter traffic getting onto the
interstate. For this reason, I‐5 experiences little congestion. At this time, approximately 41% of
vehicles have begun their evacuation trip and 15% of evacuating vehicles have successfully
evacuated the area.
At one hour after the evacuation advisory, the City of Ashland experiences peak congestion, as
shown in Figure 7‐3. Much of the city experiences gridlock as the number of evacuating vehicles
attempting to access OR‐99 exceeds its capacity. It is important to note that traffic is moving, it
is just moving slowly. Side streets experience congestion as they compete for green time at
signalized intersections and look for acceptable gaps at stop and yield signs along OR‐99. Ashland
Road (OR‐66) exhibits LOS F conditions in both east bound and west bound directions between
I‐5 and OR‐99. East bound traffic along OR‐66 exhibits LOS D conditions where there is access to
I‐5 provided by Freeway Ramps while west bound traffic continues to exhibit LOS F conditions
from I‐5 to where it meets Siskiyou Blvd (OR‐99). Oak St leaving EMZ 9 is also significantly
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congested. I‐5 continues to exhibit LOS B conditions due limited capacity ramps that meter the
traffic entering the highway. At this time, approximately 72% of vehicles have begun their
evacuation trip and approximately 28% of evacuating vehicles have successfully evacuated the
area.
At two hours after the advisory to evacuate, as shown in Figure 7‐4, congestion has dissipated
along OR‐66 between OR‐99 and I‐5, as well as along E Main St, but remains quite heavy along
many of the roadways within EMZ 2, EMZ 3, EMZ 4, EMZ 9 and EMZ 10. OR‐99 and OR‐66 remain
severely congested as vehicles continue utilizes these roadways to access I‐5. The I‐5 southbound
freeway ramp from OR‐99 continues to be a significant bottleneck causing traffic to back up all
the way into the EMZ. At this time, all external traffic is assumed to be diverted. Oak St is still
congested leaving the EMZ. Ninety‐three percent (93%) of vehicles have begun their evacuation
trip and 58% of evacuating vehicles have successfully evacuated out of the EMZs.
Congestion in the EMZ has dissipated quite a bit at three hours after the evacuation advisory, as
shown in Figure 7‐5. At this time, congestion has cleared along E Main St. Oak St is now operating
at LOS C within EMZ 9. Congestion within downtown Ashland now only remains on a small
section of Oak St and OR‐99. Severe congestion, however, remains on OR‐99 for most of the
EMZ. Congestion persists on Crowson Rd eastbound due to the stop controlled intersection with
OR‐66. At this time, approximately 98% have mobilized, and 87% of evacuating vehicles have
successfully evacuated the EMZs.
At 3 hours and 30 minutes after the advisory to evacuate, almost all congestion within the City
of Ashland has dissipated, as shown in Figure 7‐6. At this time, the only congestion remains at
the intersection of University Way and OR‐99 (due to the large number of commuting student at
SOU and the stop controlled right turn only at this intersection) and Clay St southbound at the
intersection with OR‐66 (again due to a high demand and stop controlled intersection). OR‐99 is
operating at LOS C or better. OR‐66 is operating at LOS D or better. The rest of the EMZ is clear
of congestion. At this time, nearly all vehicles (99.8%) have mobilized, and 99% of evacuating
vehicles have successfully evacuated the EMZs.
At four hours after the advisory to evacuate, all evacuees have mobilized and successfully
evacuated the EMZ as no congestion remains within the EMZ, as shown in Figure 7‐6.
7.3 Evacuation Rates
Evacuation is a continuous process, as implied by Figure 7‐8 through Figure 7‐13. These figures
indicate the rate at which traffic flows out of the indicated areas for the case of an evacuation of
all EMZs (Region R18) under the indicated conditions. One figure is presented for each scenario
considered.
The distance between the trip generation and ETE curves is the travel time. Plots of trip
generation versus ETE are indicative of the level of traffic congestion during evacuation. The
evacuation population mobilize over four hours as discussed in Section 5. This disperses evacuees
over a lengthy period of time, thus, as seen in Figure 7‐8 through , the maximum travel time
experienced is approximately 85 minutes.
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As indicated in these figures, there is typically a long "tail" to these distributions due to
mobilization and not congestion. Vehicles begin to evacuate an area slowly at first, as people
respond to the ATE at different rates. Then traffic demand builds rapidly (slopes of curves
increase). As more routes clear, the aggregate rate of egress slows since many vehicles have
already left the EMZs. Towards the end of the process, relatively few evacuation routes service
the remaining demand.
This decline in aggregate flow rate, towards the end of the process, is characterized by these
curves flattening and gradually becoming horizontal. Ideally, it would be desirable to fully
saturate all evacuation routes equally so that all will service traffic near capacity levels and all will
clear at the same time. For this ideal situation, all curves would retain the same slope until the
end – thus minimizing evacuation time. In reality, this ideal is generally unattainable reflecting
the spatial variation in population density, mobilization rates and in highway capacity over the
study area.
7.4 Evacuation Time Estimate (ETE) Results
Table 7‐1 and Table 7‐2 present the ETE values for all 18 Evacuation Regions and all 6 Evacuation
Scenarios.
Table Contents
7‐1
ETE represents the elapsed time required for 90 percent of the
population within a Region, to evacuate from that Region. All
Scenarios are considered.
7‐2
ETE represents the elapsed time required for 100 percent of the
population within a Region, to evacuate from that Region. All
Scenarios are considered.
The animation snapshots described above reflect the ETE statistics for evacuation scenarios and
regions, which are displayed in Figure 7‐2 through Figure 7‐7. Majority of the congestion is
located on major evacuation routes, OR‐66 and OR‐99 that serve a majority of the evacuating
population.
The 100th percentile ETE is 4:00 (Hours:Minutes) for all regions and scenarios. Since the trip
generation time is 4 hours, an ETE of 4:00 implies that traffic congestion clears within the EMZs
prior to the completion of mobilization time. A factor that significantly effects mobilization times
are how quickly the public can be notified of an evacuation. This study assumed notification time
of 45 minutes (see Section 5). If the evacuating population can be notified more quickly, this will
truncate mobilization times and could reduce the 100th percentile ETE. Similarly, if it takes longer
to notify the evacuation population, the 100th percentile ETE will be longer and will likely be equal
to the longer trip mobilization time. Appendix J discusses how sensitive the ETE are to changes in
mobilization time.
The 90th percentile ETE ranges between 1:20 (Hours:Minutes) and 3:10 (Hours:Minutes) for all
regions and scenarios.
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When the EMZs evacuate alone, the ETE is 1 hour and 55 minutes, on average. EMZ 4 (Region
R04) has the longest ETE when comparing against other EMZ‐only evacuations (Regions R01
through R10), specifically for midweek, midday scenarios. This is due to an anomaly caused by
the composition of demand for this region. As shown in Table 3‐11, this EMZ has the third highest
number of residents and the second lowest number of employees and tourists combined. As
discussed in Section 5, residents awaiting the return of a commuter have the longest mobilization
times and employees/tourists have the shortest. Since this EMZ has the third most ‘slowest
mobilizers’ and the second least ‘fastest mobilizers’, it actually takes longer to reach the 90th
percentile ETE. As a result, EMZ 4 has the longest 90th percentile ETE despite having a moderate
amount of total evacuating vehicles.
Alternatively, EMZ 5 has the shortest ETE when comparing against Regions R01 through R10. This
EMZ has the lowest total number of evacuating vehicles, and has more tourists (fast mobilizers)
than residents (slow mobilizers).
When looking at Regions R11 through R18, regions wherein EMZs evacuate together, Scenario 4
has the longest ETE, specifically for Regions wherein EMZ 3 and EMZ 7, and/or EMZ 4, evacuate.
This is directly caused by the large number of commuting students evacuating from SOU.
7.5 Guidance on Using ETE Tables
The user first determines the percentile of population for which the ETE is sought (federal
guidance for nuclear emergencies calls for the 90th percentile). The applicable value of ETE within
the chosen table may then be identified using the following procedure:
1. Identify the applicable Scenario:
• Season
Summer
Fall
• Day of Week
Midweek
Weekend
• Time of Day
Midday
Evening
While these Scenarios are designed, in aggregate, to represent conditions throughout the year,
some further clarification is warranted:
• The seasons are defined as follows:
Summer assumes that public schools are not in session.
Fall considers that public schools are in session.
• Time of Day: Midday implies the time over which most commuters are at work or are
travelling to/from work.
2. With the desired percentile ETE and Scenario identified, now identify the Evacuation Region:
• Determine which EMZ or combination of EMZs need to evacuate from Table 6‐1:
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Individual EMZs (R01 through R10)
Groupings/Combinations of EMZs (Region R11 through R18)
3. Determine the ETE Table based on the percentile selected. Then, for the Scenario identified
in Step 1 and the Region identified in Step 2, proceed as follows:
• The columns of Table 7‐1 and Table 7‐2 are labeled with the Scenario numbers. Identify
the proper column in the selected Table using the Scenario number defined in Step 1.
• Identify the row in this table that provides ETE values for the Region identified in Step 2.
• The unique data cell defined by the column and row so determined contains the desired
value of ETE expressed in Hours:Minutes.
Example
It is desired to identify the ETE for the following conditions:
• Wednesday, October 14th at 12:00 PM.
• It is sunny.
• The wildfire threatens northern Ashland only.
• The desired ETE is that value needed to evacuate 90 percent of the population from within
the impacted Region.
Table 7‐1 is applicable because the 90th percentile ETE is desired. Proceed as follows:
1. Identify the Scenario as fall, midweek, midday conditions. Entering Table 7‐1, it is seen
that this combination of circumstances describes Scenario 4.
2. In Table 6‐1, locate the Region that has northern Ashland only, Region R13.
3. In Table 7‐1, locate the data cell containing the value of ETE for Scenario 4 and Region
R13. This data cell is in column (4) and in the row for Region R13; it contains the ETE value
of 2:00.
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Table 7‐1. Time to Clear the Indicated Area of 90 Percent of the Affected Population
Summer Fall
Midweek Weekend Midweek
Weekend Midweek Weekend Midweek
Weekend
Scenario: (1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 – EMZ 1 1:55 1:45 1:40 1:55 1:45 1:40
R02 – EMZ 2 1:55 1:50 1:45 1:55 1:50 1:45
R03 – EMZ 3 1:40 1:50 1:45 1:35 1:50 1:45
R04 – EMZ 4 2:00 1:55 1:55 2:00 1:55 1:55
R05 – EMZ 5 1:30 1:25 1:20 1:30 1:30 1:20
R06 – EMZ 6 1:50 1:50 1:50 1:45 1:50 1:50
R07 – EMZ 7 1:50 1:50 1:50 1:50 1:50 1:50
R08 – EMZ 8 1:50 1:55 1:50 1:50 1:55 1:50
R09 – EMZ 9 1:55 1:50 1:50 1:55 1:50 1:50
R10 – EMZ 10 1:55 1:55 1:55 1:55 1:55 1:55
R11 ‐ Western Ashland 1:50 1:50 1:45 1:35 1:50 1:45
R12 ‐ Eastern Ashland 1:50 1:50 1:50 1:55 1:50 1:50
R13 ‐ Northern Ashland 2:00 1:55 1:50 2:00 1:55 1:50
R14 ‐ Central Ashland 1:45 1:50 1:45 2:30 1:50 1:45
R15 ‐ Southern Ashland 2:05 1:55 1:55 2:45 1:55 1:55
R16 ‐ Northern and Central
Ashland 1:55 1:50 1:50 2:30 1:50 1:50
R17 ‐ Southern and Central
Ashland 1:55 1:50 1:50 1:50 1:50 1:50
R18 ‐ All EMZs 2:35 2:25 2:15 3:10 2:25 2:20
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Table 7‐2. Time to Clear the Indicated Area of 100 Percent of the Affected Population
Summer Fall
Midweek Weekend Midweek
Weekend Midweek Weekend Midweek
Weekend
Scenario: (1) (2) (3) (4) (5) (6)
Region Midday Midday Evening Midday Midday Evening
R01 – EMZ 1 4:00 4:00 4:00 4:00 4:00 4:00
R02 – EMZ 2 4:00 4:00 4:00 4:00 4:00 4:00
R03 – EMZ 3 4:00 4:00 4:00 4:00 4:00 4:00
R04 – EMZ 4 4:00 4:00 4:00 4:00 4:00 4:00
R05 – EMZ 5 4:00 4:00 4:00 4:00 4:00 4:00
R06 – EMZ 6 4:00 4:00 4:00 4:00 4:00 4:00
R07 – EMZ 7 4:00 4:00 4:00 4:00 4:00 4:00
R08 – EMZ 8 4:00 4:00 4:00 4:00 4:00 4:00
R09 – EMZ 9 4:00 4:00 4:00 4:00 4:00 4:00
R10 – EMZ 10 4:00 4:00 4:00 4:00 4:00 4:00
R11 ‐ Western Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R12 ‐ Eastern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R13 ‐ Northern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R14 ‐ Central Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R15 ‐ Southern Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R16 ‐ Northern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R17 ‐ Southern and Central
Ashland 4:00 4:00 4:00 4:00 4:00 4:00
R18 ‐ All EMZs 4:00 4:00 4:00 4:00 4:00 4:00
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Figure 7‐1. Study Area Shadow Region
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Figure 7‐2. Congestion Patterns at 30 Minutes after the Advisory to Evacuate
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Figure 7‐3. Congestion Patterns at 1 Hour after the Advisory to Evacuate
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Figure 7‐4. Congestion Patterns at 2 Hours after the Advisory to Evacuate
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Figure 7‐5. Congestion Patterns at 3 Hours after the Advisory to Evacuate
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Figure 7‐6. Congestion Patterns at 3 Hours and 30 Minutes after the Advisory to Evacuate
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Figure 7‐7. Congestion Patterns at 4 Hours after the Advisory to Evacuate
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Figure 7‐8. Evacuation Time Estimates ‐ Scenario 1 for Region R18
0%
20%
40%
60%
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0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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Elapsed Time (h:mm)
ETE and Trip Generation
Summer, Midweek, Midday
(Scenario 1)
Trip Generation ETE
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Figure 7‐9. Evacuation Time Estimates ‐ Scenario 2 for Region R18
0%
20%
40%
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0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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Elapsed Time (h:mm)
ETE and Trip Generation
Summer, Weekend, Midday
(Scenario 2)
Trip Generation ETE
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Figure 7‐10. Evacuation Time Estimates ‐ Scenario 3 for Region R18
0%
20%
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0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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Elapsed Time (h:mm)
ETE and Trip Generation
Summer, Midweek, Weekend, Evening
(Scenario 3)
Trip Generation ETE
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Figure 7‐11. Evacuation Time Estimates ‐ Scenario 4 for Region R18
0%
20%
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0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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ETE and Trip Generation
Fall, Midweek, Midday
(Scenario 4)
Trip Generation ETE
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Figure 7‐12. Evacuation Time Estimates ‐ Scenario 5 for Region R18
0%
20%
40%
60%
80%
100%
0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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Elapsed Time (h:mm)
ETE and Trip Generation
Fall, Weekend, Midday
(Scenario 5)
Trip Generation ETE
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Figure 7‐13. Evacuation Time Estimates ‐ Scenario 6 for Region R18
0%
20%
40%
60%
80%
100%
0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30
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Elapsed Time (h:mm)
ETE and Trip Generation
Fall, Midweek, Weekend, Evening
(Scenario 6)
Trip Generation ETE
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8 ACCESS IMPAIRED NEIGHBORHOODS
This section details the analyses of access impaired neighborhoods within and in proximity to the
City of Ashland. Access impaired neighborhoods are areas that could have difficulty evacuating
during a wildfire emergency due to limited or narrow evacuation routes, traffic bottlenecks,
extremely low traffic flow, etc. These neighborhoods are at a higher risk during an evacuation
and should be given special consideration when planning for emergencies that require
evacuation.
In June 2020, Ashland Fire and Rescue (AFR) teamed with The Wildfire Research Center (WiRē)
to perform a Rapid Wildfire Risk Assessment (RA) of the properties within the City of Ashland1.
The RA considered a number of risk factors associated with wildfires, including access (address
posting, ingress/egress, driveway width, and driveway length), background conditions (distance
to dangerous topography, slope, and parcel exposure), defensible space (defensible space and
other combustibles), and home ignition potential (roof, siding, and attachments). The RA was
conducted at the parcel level for properties within the City of Ashland. As such, the analysis does
not include those properties and neighborhoods just outside of the city limits that could be
considered access impaired. The following approach was used to determine the access impaired
neighborhoods within and in proximity to the City of Ashland.
8.1 Data Sources
The following datasets were used to determine which neighborhoods are access impaired:
Inside the city limits
In 2018, AFR collected RA data for the 6,799 parcels within the city limits. The data collected by
the AFR was assessed for 31 wildfire‐vulnerability related attributes.
Outside the city limits
Address points for Jackson County from the Oregon GIS Portal3. These address points represent
the actual locations of structures and/or parcels.
Tax lots (aka parcels) for Jackson County from the Oregon GIS Portal4. The tax lot data includes
property type and vacancy information which was used to select the resident address points. The
tax lot was also used to determine neighborhood boundaries.
ESRI street network was used for network analysis in GIS to calculate the approximate driving
distance for each neighborhood. This data was used to help identify which areas have limited
ingress/egress routes.
1 The Wildfire Research Center Memorandum, dated June 29, 2020, subject titled “Rapid Wildfire Risk Assessment Data Scoring”.
3 https://gis.jacksoncounty.org/datasets/address-point
4 https://gis.jacksoncounty.org/datasets/tax-lots?geometry=-127.983%2C41.783%2C-117.530%2C43.201
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Wildfire risk dataset from Oregon Explorer Natural Resources Digital Library5. Data layers –
Wildfire Risk to People and Property6 and Estimated Housing Density7 – were used to select the
neighborhoods with relatively high wildfire risks and high density residential areas.
8.2 Analysis
The following methodology was used to determine which neighborhoods are access impaired
within and in proximity to the City of Ashland:
Inside the city limits
1. Each RA attribute was assigned a weighting factor and an overall risk rating based on the adjusted
WiRē’s scoring approach8. Table 1 shows the overall risk rating was used to categorize each
parcel.
2. For this analysis, any parcels categorized as “Very High” or “Extreme” were considered to be
access impaired. The results of the RA are displayed in Figure 8‐1, represented by parcel
centroids. As shown in the figure, the majority of the access impaired neighborhoods within the
city limits are along the ridgeline to the south of the city.
Outside the city limits
1. Neighborhoods with low population densities were eliminated. Tax lots define the boundaries of
residential areas; however, some tax lots, especially those in urban area are too small to be used
as neighborhoods. Therefore, the tax lots were first aggregated to map books, which have a more
reasonable scale to be considered as neighborhoods, as shown in Figure 8‐2. For example, the
size of the map book in an urban area is generally smaller than the size of a map book in a
suburban or rural area. Next, these aggregated tax lots (neighborhoods) were then superimposed
with the Estimated Housing Density layer. The neighborhoods in low density residential areas
were eliminated, as shown in Figure 8‐3. At the end of this step, 144 neighborhoods remained.
2. Neighborhoods with low wildfire risks were removed. The 144 neighborhoods with medium to
high housing densities were superimposed with the Wildfire Risk to People and Property data, as
shown in Figure 8‐4. The neighborhoods in low wildfire risk areas were removed from the
analysis. At the end of this step, 141 neighborhoods remained.
3. Identified the neighborhoods with limited means of egress. First, the resident address points were
aggregated to one central point per neighborhood, as shown in Figure 8‐5. This representative
point represents the “origin” of the neighborhood for the network analysis in GIS. A network
analysis was performed using the ESRI street data to determine the amount of roadway miles that
could be driven from each origin (neighborhood) within 10 minutes10. Then a statistical analysis
was performed on the resulting roadway mileage for each neighborhood from the network
5 https://tools.oregonexplorer.info/OE_HtmlViewer/index.html?viewer=wildfireplanning#
6 Wildfire Risk to People and Property is the product of the likelihood and consequence of wildfire on housing unit density (Where
People Live) and US Forest Service (USFS) private inholdings. This dataset considers the likelihood of fire (likelihood of burning), the
susceptibility of assets to wildfire of different intensities, and the likelihood of those intensities.
7 Estimated Housing Density is from the Oakridge National Laboratory LandScan™ population data and was obtained from the 2013
West Wide Wildfire Risk Assessment. It was developed by integrating high-resolution nighttime lights imagery and local spatial data
to identify population distributions. At approximately 1 km resolution, LandScan™ represents an ambient population (an average over
24 hours). Housing densities range from less than 1 housing unit per 40 acres, up to more than 3 housing units per acre. Areas that
are outside of urban cores and adjacent to and within wildland vegetation are of primary concern in wildfire management.
8 For detailed information of the methodology, please refer to the WiRē Memorandum.
10 The average drive time to exit the city and its surrounding residential areas under normal conditions is within 10 minutes. As such,
10 minutes was chosen as the threshold for the network analysis.
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analysis. The mean and standard deviation were computed from the data. Based on the empirical
rule, almost all observed data falls within three standard deviations from the mean for a normal
distribution. Using this rule as our basis, we looked at those neighborhoods that fell beyond 1, 2,
and 3 standard deviations from the mean. All of the data fell within 3 standard deviations. All
but 2 neighborhoods fell within 2 standard deviations. Lastly, all but 16 neighborhoods fell within
1 standard deviation. (Meaning 16 neighborhoods fall beyond 1 standard deviation from the
mean.) These 16 ‘outlier’ neighborhoods have less roadway milage within a 10 minute driving
time than the remaining 125 neighborhoods. So, in comparison to the rest of the neighborhoods
in the analysis, these 16 neighborhoods are considered to have limited means of egress. Among
the 16 access impaired neighborhoods, 11 of them are outside of the city limits.
4. The neighborhood boundaries from Step 3 were refined. Large portions of uninhabited areas
(based on aerial imagery and resident address points) were removed from the boundaries of the
11 neighborhoods. Figure 8‐6 shows the 11 neighborhoods, with refined boundaries, that were
selected as access impaired neighborhoods outside of the city limits.
Figure 8‐7 shows the results of both the inner and outer city limits access impaired neighborhood analysis.
The RA results have been aggregated into map books, to be consistent with the outer city limits analysis,
The median overall risk score has been assigned to map books that contain multiple scores.
8.3 ETE Results, Safe Refuge Areas, and Evacuation Signage
Under a fall weekday, midday scenario for an evacuation of all EMZs (worst case scenario), it
takes all of these areas 3 hours and 30 minutes to clear of congestion, as shown in Figure 7‐6.
These areas can be fully evacuated in 4 hours. Depending on the wildfire situation at hand, these
neighborhoods may require early notification to have sufficient time to evacuate before their
safety is at risk.
Given the extent of the area considered as access impaired, no safe refuge areas large enough to
safely hold this many people, in a centralized location, could be identified.
Consideration should be given regarding placement of evacuation signage, in accordance with
the MUTCD (see Section 11.3), along Frank Hill Rd, Ashland Mine Rd, Granite St, Ashland Loop
Rd, Morton St, Terrace St, Elkader St, Highwood Dr, Pinecrest Terrace, Timberlake Dr, Emigrant
Creek Rd, and E Nevada St to guide evacuees out of these neighborhoods.
Table 1. Overall Risk Rating
Risk Level Minimum Score Maximum Score
Low 30 320
Moderate 321 425
High 426 520
Very High 521 565
Extreme 566 1,000
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Figure 8‐1. Access Impaired Neighborhoods with the City Limits
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Figure 8‐2. Aggregated Tax Lots
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Figure 8‐3. Neighborhoods without Low Density Resident Areas
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Figure 8‐4. Neighborhoods without Low Density Resident Areas and Moderate to Very High Wildfire Risk to People and Property
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Figure 8‐5. Resident Address Points
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Figure 8‐6. Access Impaired Neighborhoods outside of the City Limits
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Figure 8‐7. Combined Access Impaired Neighborhoods
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9 TRANSIT‐DEPENDENT AND SPECIAL FACILITY EVACUATION TIME ESTIMATES
This section details the analyses applied and the results obtained in the form of evacuation time
estimates for transit vehicles. The demand for transit service reflects the needs of two population
groups:
• residents with no vehicles available; and
• residents of special facilities such as schools and medical facilities.
These transit vehicles mix with the general evacuation traffic that is comprised mostly of
“passenger cars” (pc’s). The presence of each transit vehicle in the evacuating traffic stream is
represented within the modeling paradigm described in Appendix D as equivalent to two pc’s.
This equivalence factor represents the longer size and more sluggish operating characteristics of
a transit vehicle, relative to those of a pc.
Transit vehicles must be mobilized in preparation for their respective evacuation missions.
Specifically:
• Bus drivers must be alerted
• They must travel to the bus depot
• They must be briefed there and assigned to a route or facility.
These activities consume time. It is estimated that bus mobilization time will average
approximately 90 minutes extending from the advisory to evacuate to the time when buses first
arrive schools, medical facilities, and transit dependent bus routes. See assumption 6 in Section
2.3.
During this mobilization period, other mobilization activities are taking place. One of these is the
action taken by parents, neighbors, relatives and friends to pick up children from school prior to
the arrival of buses, so that they may join their families. Virtually all studies of evacuations have
concluded that this “bonding” process of uniting families is universally prevalent during
emergencies and should be anticipated in the planning process. As discussed in Section 2, this
study assumes a rapidly escalating wildfire. However, local stakeholders have indicated that
children will likely be picked up by parents or guardians prior to an evacuation. Emergency plans
for the schools could not be obtained. It is generally safer if all students are loaded onto buses
and evacuated so that all students can be accounted for; when parents pick up students, it can
be a logistical nightmare especially during a high pressure situation. In addition, if buses are kept
close to the school, or are on site, it is possible that buses will be loaded and evacuated before
many parents even arrive. Since preschools and daycare facilities often do not have their own
buses and generally low enrollments, it is more feasible and safer if parents pick up these children
prior to evacuating. As such, it is assumed that children at preschool and daycare facilities are
picked up by parents or guardians prior to evacuation and that the time to perform this activity
is included in the trip generation times discussed in Section 5. This report provides estimates of
buses under the assumption that no children will be picked up by their parents, to present an
upper bound estimate of buses required.
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The procedure for computing transit dependent ETE is to:
• Estimate demand for transit service
• Estimate time to perform all transit functions
• Estimate route travel times out of the area at risk.
9.1 ETEs for Transit Dependent People
As discussed in Section 11, there are 4 bus routes designed to service the transit dependent
people residing in the EMZs. During an emergency, buses will need to be dispatched to help those
who need transportation assistance. These routes were used as representative routes to gather
this population. It is likely that during an actual evacuation, people will call the City of Ashland
Police Department and a bus will be dispatched and travel directly to the houses of those who
need assistance. If there is a shortfall of transportation resources, buses should return to the
EMZs after getting their initial passengers to safety.
When school evacuation needs are satisfied, subsequent assignments of buses to service the
transit‐dependent population should be sensitive to their mobilization time. Clearly, the buses
should be dispatched after people have completed their mobilization activities and are in a
position to board the buses when they arrive along the bus transit route.
Figure 9‐1 presents the chronology of events relevant to transit operations. The elapsed time for
each activity will now be discussed with reference to Figure 9‐1.
Activity: Mobilize Drivers (A→B→C)
Mobilization is the elapsed time from the Advisory to Evacuate until the time the buses arrive at
the facility to be evacuated. It is assumed that for a rapidly escalating emergency with no
observable indication before the fact, drivers would likely require 90 minutes to be contacted, to
travel to the depot, be briefed, and to travel to the transit‐dependent facilities/route for schools,
medical facilities, and transit dependent individuals.
Activity: Board Passengers (C→D)
A loading time of 15 minutes for school buses is used. Loading times of 1 minute, 5 minutes, and
30 minutes per patient are assumed for ambulatory patients, wheelchair bound patients, and
bedridden patients, respectively.
For multiple stops along a pick‐up route (transit‐dependent bus routes) estimation of travel time
must allow for the delay associated with stopping and starting at each pick‐up point. The time, t,
required for a bus to decelerate at a rate, “a”, expressed in ft/sec/sec, from a speed, “v”,
expressed in ft/sec, to a stop, is t = v/a. Assuming the same acceleration rate and final speed
following the stop yields a total time, T, to service boarding passengers:
𝑇ൌ𝑡𝐵𝑡ൌ𝐵2𝑡ൌ𝐵ଶ௩
,
Where B = Dwell time to service passengers. The total distance, “s” in feet, travelled during the
deceleration and acceleration activities is: s = v2/a. If the bus had not stopped to service
passengers, but had continued to travel at speed, v, then its travel time over the distance, s,
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would be: s/v = v/a. Then the total delay (i.e. pickup time, P) to service passengers is:
𝑃ൌ𝑇െ௩
ൌ𝐵௩
Assigning reasonable estimates:
• B = 50 seconds: a generous value for a single passenger, carrying personal items, to
board per stop
• v = 25 mph = 37 ft/sec
• a = 4 ft/sec/sec, a moderate average rate
Then, P ≈ 1 minute per stop. Allowing 30 minutes pick‐up time per bus run implies 30 stops per
run.
Activity: Travel to EMZ Boundaries (D→E)
Transportation resources available were provided by the city emergency management
personnel. Table 9‐1 summarizes the information received. Also included in the table are the
number of buses needed to evacuate schools, medical facilities, and transit‐dependent
population. These numbers indicate there are not sufficient resources available to evacuate
everyone in a single wave.
School Evacuation
The buses servicing the schools are ready to begin their evacuation trips at 75 minutes after the
advisory to evacuate – 90 minutes mobilization time plus 15 minutes loading time. The UNITES
software, discussed in Section 1.3, was used to define bus routes along the most likely path from
a school being evacuated to the evacuation boundary. This is done in UNITES by interactively
selecting the series of nodes from the school to the EMZ boundary. Each bus route is given an
identification number and is written to the DYNEV II input stream. DYNEV computes the route
length and outputs the average speed for each 5‐minute interval, for each bus route. The
specified bus routes are documented in Table 11‐2 (refer to the maps of the link‐node analysis
network in Appendix H for node locations). Data provided by DYNEV during the appropriate
timeframe depending on the mobilization and loading times (i.e., 90 minutes after the advisory
to evacuate) were used to compute the average speed for each route, as follows:
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑝𝑒𝑒𝑑 ൬𝑚𝑖.
ℎ𝑟൰
ൌ
⎣⎢⎢⎢⎢⎢
⎡
∑𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑙𝑖𝑛𝑘 𝑖 ሺ𝑚𝑖ሻୀଵ
∑ቐ𝐷𝑒𝑙𝑎𝑦 𝑜𝑛 𝑙𝑖𝑛𝑘 𝑖 ሺ𝑚𝑖𝑛.ሻ 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑙𝑖𝑛𝑘 𝑖 ሺ𝑚𝑖.ሻ
𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑠𝑝𝑒𝑒𝑑 𝑜𝑛 𝑙𝑖𝑛𝑘 𝑖 ቀ𝑚𝑖.ℎ𝑟.ቁ ൈ 60 𝑚𝑖𝑛.
1 ℎ𝑟.ቑୀଵ
⎦⎥⎥⎥⎥⎥
⎤
ൈ 60 𝑚𝑖𝑛.
1 ℎ𝑟.
The average speed computed (using this methodology) for the buses servicing each of the schools
in the EMZs are shown in the ETE tables for each facility. The travel time to the boundary of the
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EMZ was computed for each bus using the computed average speed and the distance to the
boundary along the most likely route out. The maximum bus speed limit within the study area
was assumed to be 55 mph.
Table 9‐2 presents the evacuation time estimates (rounded up to the nearest 5 minutes) for
schools in the EMZs. The evacuation time out of the EMZs can be computed as the sum of time
associated with Activities A→B→C, C→D, and D→E (For example: 90 min. + 15 + 45 = 2:30 for
Helman Elementary School).
Evacuation of Transit‐Dependent Population
The buses dispatched from the depots to service the transit‐dependent evacuees will be
scheduled so that they arrive at their respective routes after their passengers have completed
their mobilization. As shown in Figure 5‐8 (Residents Without Commuters), approximately 85%
of all evacuees will complete their mobilization when the buses begin their routes at 90 minutes
after the evacuation advisory.
Those buses servicing the transit‐dependent evacuees will first travel through the EMZs, then
proceed out of the area at risk. It is assumed that residents will walk to and congregate along
the roadways these routes traverse or will be picked up directly from their home, and that they
can arrive at the routes within 90 minutes after the evacuation advisory.
As previously discussed, a pickup time of 30 minutes is estimated for 30 individual stops to pick
up passengers, with an average of one minute of delay associated with each stop.
The travel distance along the respective pick‐up routes within the EMZs is estimated using the
UNITES software. Bus travel times within the EMZs are computed using average speeds
computed by DYNEV, using the aforementioned methodology that was used for school
evacuation.
Table 9‐3 present the transit‐dependent population evacuation time estimates for each bus route
calculated using the above procedures. ETE are rounded up to the nearest 5 minutes.
For example, the ETE for the bus on the route servicing EMZ 1, EMZ 2 and EMZ 10 is computed
as 90 + 35 + 30 = 2:35. Here, 35 minutes is the time to travel 5.9 miles at 15.0 mph, the average
speed output by the model for this route at 90 minutes.
Evacuation of Medical Facilities
The transit vehicle operations for this group are similar to those for school evacuation except:
• Buses are assigned on the basis of 30 patients to allow for staff to accompany the
patients.
• Basic Life Support (BLS) (ambulances) can hold 2 patient per ambulance.
• Wheelchair transport vehicles can accommodate 15 patients per wheelchair bus
Table 3‐7 indicates that 6 bus runs, 6 wheelchair bus runs, and 32 ambulance runs are needed to
service the 3 medical facilities within the EMZs.
It is estimated that mobilization time averages 90 minutes. Specially trained medical support staff
(working their regular shift) will be on site to assist in the evacuation of patients.
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Table 9‐4 summarize the ETE for medical facilities within the EMZs for normal. The distance from
each medical facility to the boundary of the EMZs was measured using GIS software and is
provided in Table 9‐4 (Dist. to EMZ Bdry). Average speeds output by the DYNEV model for Scenario
4 Region R18, capped at 55 mph, are used to compute travel time out of the area at risk. The
travel time out of the area at risk (Travel Time to Safety) is computed by dividing the travel
distance by the average travel speed. The ETE is the sum of the mobilization time, total passenger
loading time, and travel time to safety. Concurrent loading on multiple buses, wheelchair buses,
and ambulances at capacity is assumed. All ETE are rounded to the nearest 5 minutes. For
example, the calculation of ETE for Asante Ashland Community Hospital’s ambulatory patients
are:
ETE: 90 + 30 + 5 = 125 min. or 2:05.
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Table 9‐1. Summary of Transportation Needs and Resources
Transportation
Resource Buses
Wheelchair
Buses Ambulances
Resources Available
Ashland School District 19 0 0
Ashland Fire & Rescue 0 0 2
Jackson County Fire District 0 0 2‐4*
TOTAL: 19 0 2
Resources Needed
Medical Facilities (Table 3‐7): 6 6 32
Schools (Table 3‐9): 75 0 0
Transit‐Dependent Population (Table 11‐1): 4 0 0
TOTAL TRANSPORTATION NEEDS: 85 6 32
*Note: 2‐4 Ambulances can be made available from Jackson County Fire Department in the case that more are needed.
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Table 9‐2. School Evacuation Time Estimates
School
Driver
Mobilization
Time (min)
Loading
Time
(min)
Dist. To
Safety
(mi)
Average
Speed
(mph)
Travel
Time to
Safety
(min)
ETE
(hr:min)
City of Ashland, OR
Helman Elementary School 90 15 2.8 3.7 45 2:30
Ashland High School 90 15 3.2 2.6 74 3:00
Walker Elementary School 90 15 4.0 10.4 24 2:10
Ashland Middle School 90 15 5.3 11.1 29 2:15
John Muir Elementary School 90 15 0.3 10.1 2 1:50
Bellview Elementary School 90 15 6.9 21.1 20 2:05
Southern Oregon University 90 15 6.1 12.1 30 2:15
Maximum ETE: 3:00
Average ETE: 2:20
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Table 9‐3. Transit‐Dependent Evacuation Time Estimates
Bus Route
Number Bus Route
Number of
Buses
Mobilization
(min)
Route
Length
(miles)
Spee
d
(mph)
Route
Trave
l Time
(min)
Picku
p
Time
(min)
ETE
(hr:min
)
1 Servicing EMZ 1, EMZ 2 and EMZ 10 1 90 5.88 10.1 35 30 2:35
2 Servicing EMZ 3 and EMZ 4 1 90 3.98 4.8 50 30 2:50
3 Servicing EMZ 5 and EMZ 6 1 90 4.26 10.1 26 30 2:30
4 Servicing EMZ 7, EMZ 8 and EMZ 9 1 90 3.82 5.2 45 30 2:45
Maximum ETE: 2:50
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Table 9‐4. Special Facility Evacuation Time Estimates
Medical Facility Patient
Mobilization
(min)
Loading
Rate
(min per
person) People
Max
Loading
Time
(min)
Dist.
To
EMZ
Bdry
(mi)
Speed
(mph)
Travel
Time
to
Safety
(min)
ETE
(hr:min)
Asante Ashland Community Hospital
Ambulatory 90 1 63 30 2.3 26.6 5 2:05
Wheelchair Bound 90 5 31 75 2.3 29.8 5 2:50
Bedridden 90 30 31 60 2.3 22.4 6 2:40
Ashland Surgery Center
Ambulatory 90 1 13 13 2.0 20.7 6 1:50
Wheelchair Bound 90 5 6 30 2.0 38.3 3 2:05
Bedridden 90 30 6 60 2.0 38.2 3 2:35
Linda Vista Nursing Home & Rehab Center
Ambulatory 90 1 50 30 2.1 38.3 3 2:05
Wheelchair Bound 90 5 25 75 2.1 38.2 3 2:50
Bedridden 90 30 25 60 2.1 38.2 3 2:35
Maximum ETE: 2:50
Average ETE: 2:25
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Event
A Advisory to Evacuate
B Bus Dispatched from Depot
C Bus Arrives at Facility/Transit Dependent Person’s Home
D Bus Departs Facility/Transit Dependent Person’s Home
E Bus Exits Area at Risk
Activity
AB Driver Mobilization
BC Travel to Facility or Transit Dependent Person’s Home
CD Passengers Board the Bus
DE Bus Travels Toward At‐Risk Area Boundary
Figure 9‐1. Chronology of Transit Evacuation Operations
A B C D E F G
Time
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10 TRAFFIC MANAGEMENT STRATEGY
This section discusses the suggested Traffic Management Plan (TMP) that is designed to
expedite the movement of evacuating traffic. The resources required to implement this
strategy include:
• Personnel with the capabilities of performing the planned control functions of traffic
guides (preferably, not necessarily, law enforcement officers).
• Guidance is provided by the Manual of Uniform Traffic Control Devices (MUTCD)
published by the Federal Highway Administration (FHWA) of the U.S.D.O.T. All state and
most county transportation agencies have access to the MUTCD, which is available on‐
line: http://mutcd.fhwa.dot.gov which provides access to the official PDF version.
• A written plan that defines all Traffic Control Point (TCP) and Access Control Point (ACP)
locations, provides necessary details and is documented in a format that is readily
understood by those assigned to perform traffic control.
The functions to be performed in the field are:
1. Facilitate evacuating traffic movements that safely expedite travel out of the area at
risk.
2. Discourage traffic movements that move evacuating vehicles in a direction which takes
them significantly closer to the area of risk, or which interferes with the efficient flow of
other evacuees.
The terms "facilitate" and "discourage" are employed rather than "enforce" and "prohibit" to
indicate the need for flexibility in performing the traffic control function. There are always
legitimate reasons for a driver to prefer a direction other than that indicated.
For example:
• A driver may be traveling home from work or from another location, to join other family
members prior to evacuating.
• An evacuating driver may be travelling to pick up a relative, or other evacuees.
• The driver may be an emergency worker en route to perform an important activity.
ACPs are established during the evacuation to stop the flow of external traffic through the
study area. Doing so reserves the capacity on major through routes for evacuees rather than
the traffic that is passing through the area.
The implementation of a TMP must also be flexible enough for the application of sound
judgment by the traffic guide.
The TMP for this study is the outcome of the following process:
1. Evacuation simulations were run using DYNEV II to predict traffic congestion during
evacuation (see Section 7.2 and Figures 7‐2 through 7‐7).
2. These simulations help to identify the best routing and critical intersections that
experience pronounced congestion during evacuation. Any critical intersections that
would benefit from traffic or access control are suggested as TCPs. One TCP was
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identified which would benefit the evacuation: the intersection of OR‐99 and Crowson
Rd. While the overall ETE remained the same with the placement of this TCP, localized
congestion decreased. As a result, this location is recommended as a TCP.
3. Prioritization of TCPs and ACPs.
a. Application of traffic and access control at some TCPs and ACPs will have a more
pronounced influence on expediting traffic movements than at other TCPs and
ACPs. For example, TCPs controlling traffic originating from areas in close
proximity to the wildfire could have a more beneficial effect on minimizing
potential exposure to threat than those TCPs located far from the wildfire. These
priorities should be assigned by city emergency management representatives
and by law enforcement personnel.
10.1 Assumptions
The ETE calculations documented in Sections 7 and 9 assume that the recommended TMP is
implemented during evacuation.
The ETE calculations reflect the assumption that all “external‐external” trips are interdicted and
diverted after 2 hours have elapsed from the Advisory to Evacuate (ATE) to discourage through
travelers from using major through routes that traverse the study area. Dynamic and variable
message signs should be strategically positioned outside of the study area at logical diversion
points to attempt to divert traffic away from the area of risk. As such, it is assumed pass‐
through traffic that traverses the study area will diminish over the 2‐hour period.
All transit vehicles and other responders entering the EMZs to support the evacuation are
assumed to be unhindered by personnel manning TCPs and ACPs.
The ETE analysis treated the controlled intersection that is recommended as a TCP location as
being controlled by an actuated signal. In Appendix H, Table H‐2 identifies those intersections
that were modeled as TCPs.
Study assumptions 12 and 13 in Section 2.3 discuss additional TCP and ACP operational
assumptions.
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10.2 Additional Considerations
The use of Intelligent Transportation Systems (ITS) technologies can reduce the manpower and
equipment needs, while still facilitating the evacuation process. Dynamic Message Signs (DMS)
can be placed within the EMZs to provide information to travelers regarding traffic conditions,
route selection, congregation point, and reception center information. DMS placed outside of
the EMZs will warn motorists to avoid using routes that may conflict with the flow of evacuees
away from the wildfire. Highway Advisory Radio (HAR) can be used to broadcast information to
evacuees during egress through their vehicles stereo systems. Automated Travel Information
Systems (ATIS) can also be used to provide evacuees with information. Internet websites can
provide traffic and evacuation route information before the evacuee begins their trip, while the
onboard navigation systems (GPS units) and smartphones can be used to provide information
during evacuation trip.
There are only several examples of how ITS technologies can benefit the evacuation process.
Considerations should be given that ITS technologies can be used to facilitate the evacuation
process, and any additional signage placed should consider evacuation needs.
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11 EVACUATION ROUTES AND EVACUATION SIGNAGE
This section documents major evacuation routes within the study area, possible bus routes, and
suggestions on evacuation signage based on current traffic engineering standards.
11.1 Evacuation Routes
Evacuation routes are responsible for transporting EMZ evacuees and transit dependent
evacuees (schools, medical facilities, and residents who do not own or have access to a private
vehicle) to safety.
Evacuees will select routes within the EMZ in such a way as to minimize their exposure to risk.
This expectation is met by the DYNEV II model routing traffic away from the location of the
wildfire to the extent practicable. The DTRAD model satisfies this behavior by routing traffic so
as to balance traffic demand relative to the available highway capacity to the extent possible. See
Appendices B through D for further discussion. The major evacuation routes for the study area
are presented in Figure 11‐1. These routes will be used by the general population evacuating in
private vehicles and by the transit‐dependent population evacuating in buses. Transit‐dependent
evacuees will be routed to safety, outside of the evacuation area. The general population may
evacuate to some alternate destination (e.g., lodging facilities, relative’s home, campgrounds)
outside the EMZ.
The 4 representative bus routes shown graphically in Figure 11‐2 and described in Table 11‐1
were designed by KLD to service the transit dependent population in each EMZ. This does not
imply that these exact routes would be used in an emergency. It is assumed that residents will
walk to and congregate along the existing evacuation routes to flag down a bus.
The specified bus routes for all the transit‐dependent population are documented in Table 11‐2
(refer to the maps of the link‐node analysis network in Appendix H for node locations). This study
does not consider the transport of evacuees from the boundary of the evacuation region to
reception centers or congregate care centers.
Schools and medical facilities were routed along the most likely path from the facility being
evacuated to the boundary of the evacuation region. A single route was used for facilities that
would use a similar path for evacuation.
The City of Ashland should consider identifying safe shelter locations within the EMZ in an event
that an evacuation is not feasible (i.e. the fire is moving faster than an evacuation would take).
When a wildfire threat is perceived, emergency officials need to make a protective action
decision to either evacuate or shelter (at a safe refuge location) the population in imminent
danger. Safe refuge areas (i.e. hardened structures, safe open areas, water bodies) should be
predetermined and locally known in the case of an emergency.
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See Section 3.6 for more information on the transit dependent population and Section 9 for
transit dependent ETE calculations.
11.2 Evacuation Signage
Locations of evacuation signs installed along the routes shown in Figure 11‐1 for “straight‐ahead”
confirmation should be in accordance with Part 2, Chapter 2N, Section 2N.03 of the 2009
MUTCD2. These signs should display a blue circular symbol on a white square sign with a white
directional arrow and a white legend “EVACUATION ROUTE” within the blue circular symbol, as
shown in Figure 11‐3. A straight, vertical arrow pointing upward to indicate that evacuees should
continue their travel along that route should be placed on I‐5, OR‐66, and OR‐99. These
evacuation signs should be installed at one‐mile spacing along major evacuation routes.
Other signing may be placed on the approaches to major intersections to indicate the direction
of evacuation travel through the intersection. The MUTCD states that these signs should be
installed 150 to 300 feet in advance of the intersection and should indicate the turn direction
required to follow the evacuation route. These signs should display a straight horizontal arrow
pointing to the left or right, or a bent arrow pointing to the left or right, depending on the
geometrics of the approach and of the intersection, to indicate the appropriate turn movement
through the intersection needed to follow the recommended evacuation route. A through
movement will be shown as described in the previous paragraph.
Such directional signing may also be placed at the exits of special facilities to guide evacuees
toward or along recommended evacuation routes. These facilities include schools, medical
facilities, and major recreational areas, as specified in Appendix E.
Part 2, Chapter 2N of the 2009 MUTCD presents guidelines for implementing emergency
management signs. It states that “emergency management signs shall not permanently
displace any of the standard signs that are normally applicable.” While this report does not
specify the precise locations of every recommended road sign, the installation of every
evacuation route sign must comply with the guidance provided in the MUTCD.
2 Manual on Uniform Traffic Control Devices for Streets and Highways, 2009 Edition, US Department of Transportation, Federal Highway Administration
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Table 11‐1. Summary of Transit‐Dependent Bus Routes
Route
No. of
Buses Route Description
Length
(mi.)
1 1 Transit Dependent Bus Route servicing EMZ 1, EMZ 2, and EMZ 10 5.88
2 1 Transit Dependent Bus Route servicing EMZ 3 and EMZ 4 3.98
3 1 Transit Dependent Bus Route servicing EMZ 5 and EMZ 6 4.26
4 1 Transit Dependent Bus Route servicing EMZ 7, EMZ 8, and EMZ 9 3.82
Total: 4
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Table 11‐2. Bus Route Description
Bus
Route
Number Description Nodes Traversed from Route Start to EPZ Boundary
1 Walker Elementary School 124, 108, 110, 113, 323, 9, 7, 8
2 Helman Elementary School 161, 162, 261, 50, 51, 52, 159, 53, 54, 55
4 Ashland High School 127, 41, 233, 42, 237, 240, 43, 44, 148, 238, 45, 227, 46, 254, 47, 241, 48, 49, 50, 51, 52,
159, 53, 54, 55
6 John Muir Outdoor School 124, 108, 110, 113, 323, 9, 7, 8
7 Bellview Elementary School 40, 308, 307, 9, 7, 8
9 Ashland Surgery Center 51, 52, 159, 53, 54, 55
10 Linda Vista Nursing and Rehab
Center 51, 52, 159, 53, 54, 55
11 Oregon Child Development 215, 48, 49, 50, 51, 52, 159, 53, 54, 55
12 Reflective Hearts Childcare 133, 143, 134, 148, 238, 45, 227, 46, 254, 47, 241, 48, 49, 50, 51, 52, 159, 53, 54, 55
13 Stone Soup Playschool 114, 311, 312, 113, 323, 9, 7, 8
14 Pea Pod Village 143, 134, 148, 238, 45, 227, 46, 254, 47, 241, 48, 49, 50, 51, 52, 159, 53, 54, 55
15 Childrens World 233, 42, 237, 240, 43, 44, 148, 238, 45, 227, 46, 254, 47, 241, 48, 49, 50, 51, 52, 159, 53,
54, 55
16 Memory Lane Preschool 109, 108, 110, 113, 323, 9, 7, 8
17 Head Start 108, 110, 113, 323, 9, 7, 8
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18 Rain and Shine Preschool 267, 111, 275, 315, 110, 113, 323, 9, 7, 8
3 Transit Dependent Bus Route for
EMZ 1, EMZ 2, and EMZ 10 290, 284, 283, 149, 150, 49, 50, 51, 52, 159, 53, 54, 55
20 Transit Dependent Bus Route for
EMZ 3 and EMZ 4 268, 135, 235, 234, 41, 339, 123, 70, 108, 110, 113, 323, 9, 7, 8
23 Transit Dependent Bus Route for
EMZ 5 and EMZ 6 275, 315, 110, 113, 323, 9, 7, 8
26 Transit Dependent Bus Route for
EMZ 7, EMZ 8, and EMZ 9 226, 224, 225, 144, 147, 145, 122, 121, 146, 49, 50, 51, 52, 159, 53, 54, 55
8 Asante Ashland Community Hospital 119, 51, 52, 159, 53, 54, 55
5 Ashland Middle School 124, 108, 110, 113, 323, 9, 7, 8
27 TD Zone 10 281, 282, 161, 162, 261, 50, 51, 52, 159, 53, 54, 55
28 Southern Oregon University 123, 70, 108, 110, 113, 323, 9, 7, 8
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Figure 11‐1. Evacuation Route Map
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Figure 11‐2. Transit‐Dependent Bus Routes Servicing the EMZ
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Figure 11‐3. Evacuation Route Sign Example
APPENDIX A
Glossary of Traffic Engineering Terms
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A. GLOSSARY OF TRAFFIC ENGINEERING TERMS
This appendix provides a glossary of traffic engineering terms that are used throughout this
report.
Table A‐1. Glossary of Traffic Engineering Terms
Term Definition
Analysis Network
A graphical representation of the geometric topology of a physical
roadway system, which is comprised of directional links and
nodes.
Link A network link represents a specific, one‐directional section of
roadway. A link has both physical (length, number of lanes,
topology, etc.) and operational (turn movement percentages,
service rate, free‐flow speed) characteristics.
Measures of Effectiveness Statistics describing traffic operations on a roadway network.
Node A network node generally represents an intersection of network
links. A node has control characteristics, i.e., the allocation of
service time to each approach link.
Origin A location attached to a network link, within the EMZ or Shadow
Region, where trips are generated at a specified rate in vehicles
per hour (vph). These trips enter the roadway system to travel to
their respective destinations.
Prevailing Roadway and
Traffic Conditions
Relates to the physical features of the roadway, the nature (e.g.,
composition) of traffic on the roadway and the ambient conditions
(weather, visibility, pavement conditions, etc.).
Service Rate
Maximum rate at which vehicles, executing a specific turn
maneuver, can be discharged from a section of roadway at the
prevailing conditions, expressed in vehicles per second (vps) or
vehicles per hour (vph).
Service Volume
Maximum number of vehicles which can pass over a section of
roadway in one direction during a specified time period with
operating conditions at a specified Level of Service (The Service
Volume at the upper bound of Level of Service, E, equals Capacity).
Service Volume is usually expressed as vehicles per hour (vph).
Signal Cycle Length The total elapsed time to display all signal indications, in sequence.
The cycle length is expressed in seconds.
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Term Definition
Signal Interval
A single combination of signal indications. The interval duration is
expressed in seconds. A signal phase is comprised of a sequence
of signal intervals, usually green, yellow, red.
Signal Phase
A set of signal indications (and intervals) which services a
particular combination of traffic movements on selected
approaches to the intersection. The phase duration is expressed
in seconds.
Traffic (Trip) Assignment
A process of assigning traffic to paths of travel in such a way as to
satisfy all trip objectives (i.e., the desire of each vehicle to travel
from a specified origin in the network to a specified destination)
and to optimize some stated objective or combination of
objectives. In general, the objective is stated in terms of
minimizing a generalized "cost". For example, "cost" may be
expressed in terms of travel time.
Traffic Density
The number of vehicles that occupy one lane of a roadway section
of specified length at a point in time, expressed as vehicles per
mile (vpm).
Traffic (Trip) Distribution
A process for determining the destinations of all traffic generated
at the origins. The result often takes the form of a Trip Table,
which is a matrix of origin‐destination traffic volumes.
Traffic Simulation
A computer model designed to replicate the real‐world operation
of vehicles on a roadway network, so as to provide statistics
describing traffic performance. These statistics are called
Measures of Effectiveness (MOE).
Traffic Volume
The number of vehicles that pass over a section of roadway in one
direction, expressed in vehicles per hour (vph). Where applicable,
traffic volume may be stratified by turn movement.
Travel Mode Distinguishes between private auto, bus, rail, pedestrian and air
travel modes.
Trip Table or Origin‐
Destination Matrix
A rectangular matrix or table, whose entries contain the number
of trips generated at each specified origin, during a specified time
period, that are attracted to (and travel toward) each of its
specified destinations. These values are expressed in vehicles per
hour (vph) or in vehicles.
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Term Definition
Turning Capacity
The capacity associated with that component of the traffic stream
which executes a specified turn maneuver from an approach at an
intersection.
APPENDIX B
DTRAD: Dynamic Traffic Assignment and Distribution Model
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B. DYNAMIC TRAFFIC ASSIGNMENT AND DISTRIBUTION MODEL
This appendix describes the integrated dynamic trip assignment and distribution model named
DTRAD (Dynamic Traffic Assignment and Distribution) that is expressly designed for use in
analyzing evacuation scenarios. DTRAD employs logit‐based path‐choice principles and is one
of the models of the DYNEV II System. The DTRAD module implements path‐based Dynamic
Traffic Assignment (DTA) so that time dependent Origin‐Destination (OD) trips are “assigned” to
routes over the network based on prevailing traffic conditions.
To apply the DYNEV II System, the analyst must specify the highway network, link capacity
information, the time‐varying volume of traffic generated at all origin centroids and, optionally,
a set of accessible candidate destination nodes on the periphery of the EMZ for selected
origins. DTRAD calculates the optimal dynamic trip distribution (i.e., trip destinations) and the
optimal dynamic trip assignment (i.e., trip routing) of the traffic generated at each origin node
traveling to its set of candidate destination nodes, so as to minimize evacuee travel “cost”.
Overview of Integrated Distribution and Assignment Model
The underlying premise is that the selection of destinations and routes is intrinsically coupled in
an evacuation scenario. That is, people in vehicles seek to travel out of an area of potential risk
as rapidly as possible by selecting the “best” routes. The model is designed to identify these
“best” routes in a manner that realistically distributes vehicles from origins to destinations and
routes them over the highway network, in a consistent and optimal manner, reflecting evacuee
behavior.
For each origin, a set of “candidate destination nodes” is selected by the software logic and by
the analyst to reflect the desire by evacuees to travel away from the wildfire and to access
major highways. The specific destination nodes within this set that are selected by travelers
and the selection of the connecting paths of travel, are both determined by DTRAD. This
determination is made by a logit‐based path choice model in DTRAD, so as to minimize the trip
“cost”, as discussed later.
The traffic loading on the network and the consequent operational traffic environment of the
network (density, speed, throughput on each link) vary over time as the evacuation takes place.
The DTRAD model, which is interfaced with the DYNEV simulation model, executes a succession
of “sessions” wherein it computes the optimal routing and selection of destination nodes for
the conditions that exist at that time.
Interfacing the DYNEV Simulation Model with DTRAD
The DYNEV II system reflects evacuation behavior wherein evacuees will seek to travel in a
general direction away from the location of the hazardous event. An algorithm was developed
to support the DTRAD model in dynamically varying the Trip Table (O‐D matrix) over time from
one DTRAD session to the next. Another algorithm executes a “mapping” from the specified
“geometric” network (link‐node analysis network) that represents the physical highway system,
to a “path” network that represents the vehicle [turn] movements. DTRAD computations are
performed on the “path” network: DYNEV simulation model, on the “geometric” network.
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DTRAD Description
DTRAD is the DTA module for the DYNEV II System.
When the road network under study is large, multiple routing options are usually available
between trip origins and destinations. The problem of loading traffic demands and propagating
them over the network links is called Network Loading and is addressed by DYNEV II using
macroscopic traffic simulation modeling. Traffic assignment deals with computing the
distribution of the traffic over the road network for given O‐D demands and is a model of the
route choice of the drivers. Travel demand changes significantly over time, and the road
network may have time dependent characteristics, e.g., time‐varying signal timing or reduced
road capacity because of lane closure, or traffic congestion. To consider these time
dependencies, DTA procedures are required.
The DTRAD DTA module represents the dynamic route choice behavior of drivers, using the
specification of dynamic origin‐destination matrices as flow input. Drivers choose their routes
through the network based on the travel cost they experience (as determined by the simulation
model). This allows traffic to be distributed over the network according to the time‐dependent
conditions. The modeling principles of DTRAD include:
It is assumed that drivers not only select the best route (i.e., lowest cost path) but some
also select less attractive routes. The algorithm implemented by DTRAD archives several
“efficient” routes for each O‐D pair from which the drivers choose.
The choice of one route out of a set of possible routes is an outcome of “discrete choice
modeling”. Given a set of routes and their generalized costs, the percentages of drivers
that choose each route is computed. The most prevalent model for discrete choice
modeling is the logit model. DTRAD uses a variant of Path‐Size‐Logit model (PSL). PSL
overcomes the drawback of the traditional multinomial logit model by incorporating an
additional deterministic path size correction term to address path overlapping in the
random utility expression.
DTRAD executes the Traffic Assignment (TA) algorithm on an abstract network
representation called "the path network" which is built from the actual physical link‐
node analysis network. This execution continues until a stable situation is reached: the
volumes and travel times on the edges of the path network do not change significantly
from one iteration to the next. The criteria for this convergence are defined by the user.
Travel “cost” plays a crucial role in route choice. In DTRAD, path cost is a linear
summation of the generalized cost of each link that comprises the path. The generalized
cost for a link, a, is expressed as
𝑐 ൌ𝛼𝑡 𝛽𝑙 𝛾𝑠,
Where 𝑐 is the generalized cost for link a and 𝛼, 𝛽, and 𝛾 are cost coefficients for link
travel time, distance, and supplemental cost, respectively. Distance and supplemental
costs are defined as invariant properties of the network model, while travel time is a
dynamic property dictated by prevailing traffic conditions. The DYNEV simulation model
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computes travel times on all edges in the network and DTRAD uses that information to
constantly update the costs of paths. The route choice decision model in the next
simulation iteration uses these updated values to adjust the route choice behavior. This
way, traffic demands are dynamically re‐assigned based on time dependent conditions.
The interaction between the DTRAD traffic assignment and DYNEV II simulation models
is depicted in Figure B‐1. Each round of interaction is called a Traffic Assignment Session
(TA session). A TA session is composed of multiple iterations, marked as loop B in the
figure.
The supplemental cost is based on the “survival distribution” (a variation of the
exponential distribution). The Inverse Survival Function is a “cost” term in DTRAD to
represent the potential risk of travel toward the wildfire:
sa = ‐ β ln (p), 0 ≤ p ≤ l ; β 0
p = ௗ
ௗబ
dn = Distance of node, n, from the wildfire
d0 = Distance from the wildfire where there is zero risk
β = Scaling factor
A do was chosen such that the EMZs are within the area at risk. Note that the supplemental
cost, sa, of link, a, is (high, low), if its downstream node, n, is (near, far from) the wildfire.
Network Equilibrium
In 1952, John Wardrop wrote:
Under equilibrium conditions traffic arranges itself in congested networks in such a way
that no individual trip‐maker can reduce his path costs by switching routes.
The above statement describes the “User Equilibrium” definition, also called the “Selfish Driver
Equilibrium”. It is a hypothesis that represents a [hopeful] condition that evolves over time as
drivers search out alternative routes to identify those routes that minimize their respective
“costs”. It has been found that this “equilibrium” objective to minimize costs is largely realized
by most drivers who routinely take the same trip over the same network at the same time (i.e.,
commuters). Effectively, such drivers “learn” which routes are best for them over time. Thus,
the traffic environment “settles down” to a near‐equilibrium state.
Clearly, since an emergency evacuation is a sudden, unique event, it does not constitute a long‐
term learning experience which can achieve an equilibrium state. Consequently, DTRAD was
not designed as an equilibrium solution, but to represent drivers in a new and unfamiliar
situation, who respond in a flexible manner to real‐time information (either broadcast or
observed) in such a way as to minimize their respective costs of travel.
City of Ashland B‐4 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure B‐1. Flow Diagram of Simulation‐DTRAD Interface
Start of next DTRAD Session
Set T0 ൌ Clock time.
Archive System State at T0
Define latest Link Turn
Percentages
Execute Simulation Model from
time, T0 to T1 (burn time)
Provide DTRAD with link MOE at
time, T1
Execute DTRAD iteration;
Get new Turn Percentages
Retrieve System State at T0 ;
Apply new Link Turn Percents
DTRAD iteration converges?
Next iteration Simulate from T0 to T2
(DTA session duration)
Set Clock to T2
A
B
A
Yes No
B
APPENDIX C
DYNEV Traffic Simulation Model
City of Ashland C‐1 KLD Engineering, P.C.
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C. DYNEV TRAFFIC SIMULATION MODEL
This appendix describes the DYNEV traffic simulation model. The DYNEV traffic simulation
model is a macroscopic model that describes the operations of traffic flow in terms of aggregate
variables: vehicles, flow rate, mean speed, volume, density, queue length, on each link, for
each turn movement, during each Time Interval (simulation time step). The model generates
trips from “sources” and from Entry Links and introduces them onto the analysis network at
rates specified by the analyst based on the mobilization time distributions. The model simulates
the movements of all vehicles on all network links over time until the network is empty. At
intervals, the model outputs Measures of Effectiveness (MOE) such as those listed in Table C‐1.
Model Features Include:
Explicit consideration is taken of the variation in density over the time step; an iterative
procedure is employed to calculate an average density over the simulation time step for
the purpose of computing a mean speed for moving vehicles.
Multiple turn movements can be serviced on one link; a separate algorithm is used to
estimate the number of (fractional) lanes assigned to the vehicles performing each turn
movement, based, in part, on the turn percentages provided by the DTRAD model.
At any point in time, traffic flow on a link is subdivided into two classifications: queued
and moving vehicles. The number of vehicles in each classification is computed. Vehicle
spillback, stratified by turn movement for each network link, is explicitly considered and
quantified. The propagation of stopping waves from link to link is computed within each
time step of the simulation. There is no “vertical stacking” of queues on a link.
Any link can accommodate “source flow” from zones via side streets and parking
facilities that are not explicitly represented. This flow represents the evacuating trips
that are generated at the source.
The relation between the number of vehicles occupying the link and its storage capacity
is monitored every time step for every link and for every turn movement. If the
available storage capacity on a link is exceeded by the demand for service, then the
simulator applies a “metering” rate to the entering traffic from both the upstream
feeders and source node to ensure that the available storage capacity is not exceeded.
A “path network” that represents the specified traffic movements from each network
link is constructed by the model; this path network is utilized by the DTRAD model.
A two‐way interface with DTRAD: (1) provides link travel times; (2) receives data that
translates into link turn percentages.
Provides MOE to animation software, EVAN
Calculates ETE statistics
All traffic simulation models are data‐intensive. Table C‐2 outlines the necessary input data
elements.
City of Ashland C‐2 KLD Engineering, P.C.
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To provide an efficient framework for defining these specifications, the physical highway
environment is represented as a network. The unidirectional links of the network represent
roadway sections: rural, multi‐lane, urban streets or freeways. The nodes of the network
generally represent intersections or points along a section where a geometric property changes
(e.g. a lane drop, change in grade or free flow speed).
Figure C‐1 is an example of a small network representation. The freeway is defined by the
sequence of links, (20,21), (21,22), and (22,23). Links (8001, 19) and (3, 8011) are Entry and Exit
links, respectively. An arterial extends from node 3 to node 19 and is partially subsumed within
a grid network. Note that links (21,22) and (17,19) are grade‐separated.
C.1 Methodology
C.1.1 The Fundamental Diagram
It is necessary to define the fundamental diagram describing flow‐density and speed‐density
relationships. Rather than “settling for” a triangular representation, a more realistic
representation that includes a “capacity drop”, (I‐R)Qmax, at the critical density when flow
conditions enter the forced flow regime, is developed and calibrated for each link. This
representation, shown in Figure C‐2, asserts a constant free speed up to a density, k, and then
a linear reduction in speed in the range, k k kୡ ൌ 45 vpm, the density at capacity. In the
flow‐density plane, a quadratic relationship is prescribed in the range, kୡ ൏𝑘kୱ ൌ 95 vpm
which roughly represents the “stop‐and‐go” condition of severe congestion. The value of flow
rate, Qୱ , corresponding to kୱ, is approximated at 0.7 RQ୫ୟ୶ . A linear relationship
between kୱ and k୨ completes the diagram shown in Figure C‐2. Table C‐3 is a glossary of terms.
The fundamental diagram is applied to moving traffic on every link. The specified calibration
values for each link are: (1) Free speed, v ; (2) Capacity, Q୫ୟ୶ ; (3) Critical density, kୡ ൌ
45 vpm ; (4) Capacity Drop Factor, R = 0.9 ; (5) Jam density, k୨ . Then, vୡ ൌ ୕ౣ౮
୩ౙ ,kൌ kୡ െ
ሺିౙሻ ୩ౙమ
୕ౣ౮ . Setting kത ൌ k െ kୡ , then Q ൌ RQ୫ୟ୶ െ ୖ୕ౣ౮
଼ଷଷଷ kതଶ for 0 kത kതୱ ൌ 50 . It can be
shown that Q ൌ൫0.98 െ 0.0056 kത൯ RQ୫ୟ୶ for kതୱ kത kത୨,where kതୱ ൌ 50 and kഥ ൌ 175.
C.1.2 The Simulation Model
The simulation model solves a sequence of “unit problems”. Each unit problem computes the
movement of traffic on a link, for each specified turn movement, over a specified time interval
(TI) which serves as the simulation time step for all links. Figure C‐3 is a representation of the
unit problem in the time‐distance plane. Table C‐3 is a glossary of terms that are referenced in
the following description of the unit problem procedure.
The formulation and the associated logic presented below are designed to solve the unit
problem for each sweep over the network (discussed below), for each turn movement serviced
on each link that comprises the evacuation network, and for each TI over the duration of the
evacuation.
City of Ashland C‐3 KLD Engineering, P.C.
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Given ൌ Qୠ ,Mୠ ,L ,TI ,E ,LN ,G Cൗ ,h ,L୴ ,R ,Lୡ ,E ,M
Compute ൌ O ,Qୣ ,Mୣ
Define O ൌ O୕ O O ; E ൌ Eଵ Eଶ
1. For the first sweep, s = 1, of this TI, get initial estimates of mean density, k , the R – factor,
R and entering traffic, E , using the values computed for the final sweep of the prior TI.
For each subsequent sweep, s 1 ,calculate E ൌ ∑P୧୧ O୧ S where P୧ ,O୧ are the
relevant turn percentages from feeder link, i , and its total outflow (possibly metered) over
this TI; S is the total source flow (possibly metered) during the current TI.
Set iteration counter, n = 0, k ൌ k ,and Eൌ E .
2. Calculate v ሺkሻ such that k 130 using the analytical representations of the
fundamental diagram.
Calculate Cap ൌ Q୫ୟ୶ሺTIሻ
3600 ൫G Cൗ൯ LN ,in vehicles,this value may be reduced
due to metering
Set R ൌ 1.0 if G Cൗ ൏ 1 or if k kୡ ; Set Rൌ 0.9 only if G Cൗ ൌ 1 and k kୡ
Calculate queue length, Lୠ ൌ Qୠ L୴
LN
3. Calculate tଵ ൌ TI െ
. If tଵ ≺0 , set tଵ ൌ Eଵ ൌ O ൌ 0 ; Else, Eଵ ൌ E ୲భ
୍ .
4. Then Eଶ ൌ E െ Eଵ ; tଶ ൌ TI െ tଵ
5. If Qୠ Cap ,then
O୕ ൌ Cap ,O ൌ O ൌ 0
If tଵ 0 ,then
Qୣᇱ ൌ Qୠ Mୠ Eଵ െ Cap
Else
Qୣᇱ ൌ Qୠ െ Cap
End if
Calculate Qୣ and Mୣ using Algorithm A ሺbelowሻ
6. Else ሺQୠ ≺Capሻ
O୕ ൌ Qୠ ,RCapൌCap െ O୕
7. If Mୠ RCap ,then
8. If tଵ 0 , O ൌ Mୠ ,O ൌ min ൬RCap െ Mୠ,tଵ Cap
TI ൰ 0
Qୣᇱ ൌ Eଵ െ O
If Qୣᇱ 0 ,then
City of Ashland C‐4 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Calculate Qୣ ,Mୣ with Algorithm A
Else
Qୣ ൌ 0 , Mୣ ൌ Eଶ
End if
Else ሺtଵ ൌ 0ሻ
O ൌቀ୴ሺ୍ሻିౘ
ିౘ ቁ Mୠ and O ൌ 0
Mୣ ൌ Mୠ െ O E ; Qୣ ൌ 0
End if
9. Else ሺMୠ 𝑅𝐶𝑎𝑝ሻ
O ൌ 0
If tଵ 0 , then
O ൌ RCap , Qୣᇱ ൌ Mୠ െ O Eଵ
Calculate Qୣ and Mୣ using Algorithm A
10. Else ሺtଵ ൌ 0ሻ
Mୢ ൌቂቀ୴ሺ୍ሻିౘ
ିౘ ቁ Mୠ ቃ
If Mୢ 𝑅𝐶𝑎𝑝 ,𝑡ℎ𝑒𝑛
O ൌ RCap
Qୣᇱ ൌ Mୢ െ O
Apply Algorithm A to calculate Qୣ and Mୣ
Else
O ൌ Mୢ
Mୣ ൌ Mୠ െ O E and Qୣ ൌ 0
End if
End if
End if
End if
11. Calculate a new estimate of average density, k
ത୬ ൌ ଵ
ସ ሾkୠ 2 k୫ kୣሿ ,
where kୠ = density at the beginning of the TI
kୣ = density at the end of the TI
k୫ = density at the mid‐point of the TI
All values of density apply only to the moving vehicles.
If หkത୬ െ kത୬ିଵห ∈and n ൏ N
where N ൌ max number of iterations, and ϵ is a convergence criterion,then
12. set n ൌ n 1 , and return to step 2 to perform iteration,n,using k ൌ kത୬ .
End if
Computation of unit problem is now complete. Check for excessive inflow causing
spillback.
City of Ashland C‐5 KLD Engineering, P.C.
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T
t3
Q’
t1
L3
v
Qe
v
vQ Qb
Mb
13. If Qୣ Mୣ ሺିሻ
౬ , then
The number of excess vehicles that cause spillback is: SB ൌ Qୣ Mୣ െ ሺିሻ ∙
౬
,
where W is the width of the upstream intersection. To prevent spillback, meter the
outflow from the feeder approaches and from the source flow, S, during this TI by the
amount, SB. That is, set
M ൌ 1 െ SB
ሺE Sሻ 0 ,where M is the metering factor ሺover all movementsሻ.
This metering factor is assigned appropriately to all feeder links and to the source flow, to be
applied during the next network sweep, discussed later.
Algorithm A
This analysis addresses the flow environment over a TI during which moving vehicles can
join a standing or discharging queue. For the case
shown, Qୠ Cap,with tଵ 0 and a queue of
length, Qୣᇱ , formed by that portion of Mୠ and E that
reaches the stop‐bar within the TI, but could not
discharge due to inadequate capacity. That is, Qୠ
Mୠ Eଵ 𝐶𝑎𝑝. This queue length, Qୣᇱ ൌ Qୠ
Mୠ Eଵ െ Cap can be extended to Qୣ by traffic
entering the approach during the current TI, traveling
at speed, v, and reaching the rear of the queue within
the TI. A portion of the entering vehicles, Eଷ ൌ E ୲య
୍ ,
will likely join the queue. This analysis calculates
tଷ ,Qୣ and Mୣ for the input values of L, TI, v, E, t, L୴, LN, Qୣᇱ .
When tଵ 0 and Qୠ Cap:
Define: Lୣᇱ ൌ Qୣᇱ L୴
LN . From the sketch, Lଷ ൌ vሺTI െ tଵ െ tଷሻ ൌ L െ ሺQୣᇱ Eଷሻ L୴
LN .
Substituting Eଷ ൌ ୲య
୍ E yields: െ vtଷ ୲య
୍ E ౬
ൌ L െ vሺTI െ tଵሻ െ Lୣᇱ . Recognizing that
the first two terms on the right hand side cancel, solve for tଷ to obtain:
tଷ ൌ Lୣᇱ
ቂv െ ETI L୴LNቃ
such that 0 tଷ TI െ tଵ
If the denominator, ቂv െ
୍ ౬
ቃ0,set tଷ ൌ TI െ tଵ .
Then,Qୣ ൌ Qୣᇱ E tଷ
TI , Mୣ ൌ E ൬1 െ tଵ tଷ
TI ൰
The complete Algorithm A considers all flow scenarios; space limitation precludes its
inclusion, here.
City of Ashland C‐6 KLD Engineering, P.C.
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C.1.3 Lane Assignment
The “unit problem” is solved for each turn movement on each link. Therefore it is necessary to
calculate a value, LN୶ , of allocated lanes for each movement, x. If in fact all lanes are specified
by, say, arrows painted on the pavement, either as full lanes or as lanes within a turn bay, then
the problem is fully defined. If however there remain un‐channelized lanes on a link, then an
analysis is undertaken to subdivide the number of these physical lanes into turn movement
specific virtual lanes, LNx.
C.2 Implementation
C.2.1 Computational Procedure
The computational procedure for this model is shown in the form of a flow diagram as Figure
C‐4. As discussed earlier, the simulation model processes traffic flow for each link
independently over TI that the analyst specifies; it is usually 60 seconds or longer. The first step
is to execute an algorithm to define the sequence in which the network links are processed so
that as many links as possible are processed after their feeder links are processed, within the
same network sweep. Since a general network will have many closed loops, it is not possible to
guarantee that every link processed will have all of its feeder links processed earlier.
The processing then continues as a succession of time steps of duration, TI, until the simulation
is completed. Within each time step, the processing performs a series of “sweeps” over all
network links; this is necessary to ensure that the traffic flow is synchronous over the entire
network. Specifically, the sweep ensures continuity of flow among all the network links; in the
context of this model, this means that the values of E, M, and S are all defined for each link such
that they represent the synchronous movement of traffic from each link to all of its outbound
links. These sweeps also serve to compute the metering rates that control spillback.
Within each sweep, processing solves the “unit problem” for each turn movement on each link.
With the turn movement percentages for each link provided by the DTRAD model, an algorithm
allocates the number of lanes to each movement serviced on each link. The timing at a signal, if
any, applied at the downstream end of the link, is expressed as a G/C ratio, the signal timing
needed to define this ratio is an input requirement for the model. The model also has the
capability of representing, with macroscopic fidelity, the actions of actuated signals responding
to the time‐varying competing demands on the approaches to the intersection.
The solution of the unit problem yields the values of the number of vehicles, O, that discharge
from the link over the time interval and the number of vehicles that remain on the link at the
end of the time interval as stratified by queued and moving vehicles: Qୣ and Mୣ . The
procedure considers each movement separately (multi‐piping). After all network links are
processed for a given network sweep, the updated consistent values of entering flows, E;
metering rates, M; and source flows, S are defined so as to satisfy the “no spillback” condition.
The procedure then performs the unit problem solutions for all network links during the
following sweep.
City of Ashland C‐7 KLD Engineering, P.C.
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Experience has shown that the system converges (i.e. the values of E, M and S “settle down” for
all network links) in just two sweeps if the network is entirely under‐saturated or in four sweeps
in the presence of extensive congestion with link spillback. (The initial sweep over each link
uses the final values of E and M, of the prior TI). At the completion of the final sweep for a TI,
the procedure computes and stores all measures of effectiveness for each link and turn
movement for output purposes. It then prepares for the following time interval by defining the
values of Qୠ and Mୠ for the start of the next TI as being those values of Qୣ and Mୣ at the end
of the prior TI. In this manner, the simulation model processes the traffic flow over time until
the end of the run. Note that there is no space‐discretization other than the specification of
network links.
C.2.2 Interfacing with Dynamic Traffic Assignment (DTRAD)
The DYNEV II system reflects evacuation behavior wherein evacuees will seek to travel in a
general direction away from the location of the hazardous event. Thus, an algorithm was
developed to identify an appropriate set of destination nodes for each origin based on its
location and on the expected direction of travel. This algorithm also supports the DTRAD model
in dynamically varying the Trip Table (O‐D matrix) over time from one DTRAD session to the
next.
Figure B‐1 depicts the interaction of the simulation model with the DTRAD model in the DYNEV
II system. As indicated, DYNEV II performs a succession of DTRAD “sessions”; each such session
computes the turn link percentages for each link that remain constant for the session duration,
ሾT ,Tଶሿ , specified by the analyst. The end product is the assignment of traffic volumes from
each origin to paths connecting it with its destinations in such a way as to minimize the
network‐wide cost function. The output of the DTRAD model is a set of updated link turn
percentages which represent this assignment of traffic.
As indicated in Figure B‐1, the simulation model supports the DTRAD session by providing it
with operational link MOE that are needed by the path choice model and included in the
DTRAD cost function. These MOE represent the operational state of the network at a time,
Tଵ Tଶ , which lies within the session duration, ሾT ,Tଶሿ . This “burn time”, Tଵ െ T , is
selected by the analyst. For each DTRAD iteration, the simulation model computes the change
in network operations over this burn time using the latest set of link turn percentages
computed by the DTRAD model. Upon convergence of the DTRAD iterative procedure, the
simulation model accepts the latest turn percentages provided by the Dynamic Traffic
Assignment (DTA) model, returns to the origin time, T , and executes until it arrives at the end
of the DTRAD session duration at time, Tଶ . At this time the next DTA session is launched and
the whole process repeats until the end of the DYNEV II run.
Additional details are presented in Appendix B.
City of Ashland C‐8 KLD Engineering, P.C.
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Table C‐1. Selected Measures of Effectiveness Output by DYNEV II
Measure Units Applies To
Vehicles Discharged Vehicles Link, Network, Exit Link
Speed Miles/Hours (mph) Link, Network
Density Vehicles/Mile/Lane Link
Level of Service LOS Link
Content Vehicles Network
Travel Time Vehicle‐hours Network
Evacuated Vehicles Vehicles Network, Exit Link
Trip Travel Time Vehicle‐minutes/trip Network
Capacity Utilization Percent Exit Link
Attraction Percent of total evacuating vehicles Exit Link
Max Queue Vehicles Node, Approach
Time of Max Queue Hours:minutes Node, Approach
Route Statistics Length (mi); Mean Speed (mph); Travel
Time (min) Route
Mean Travel Time Minutes Evacuation Trips; Network
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Table C‐2. Input Requirements for the DYNEV II Model
HIGHWAY NETWORK
Links defined by upstream and downstream node numbers
Link lengths
Number of lanes (up to 9) and channelization
Turn bays (1 to 3 lanes)
Destination (exit) nodes
Network topology defined in terms of downstream nodes for each receiving link
Node Coordinates (X,Y)
Wildfire Coordinates (X,Y)
GENERATED TRAFFIC VOLUMES
On all entry links and source nodes (origins), by Time Period
TRAFFIC CONTROL SPECIFICATIONS
Traffic signals: link‐specific, turn movement specific
Signal control treated as fixed time or actuated
Location of traffic control points (these are represented as actuated signals)
Stop and Yield signs
Right‐turn‐on‐red (RTOR)
Route diversion specifications
Turn restrictions
Lane control (e.g. lane closure, movement‐specific)
DRIVER’S AND OPERATIONAL CHARACTERISTICS
Driver’s (vehicle‐specific) response mechanisms: free‐flow speed, discharge headway
Bus route designation.
DYNAMIC TRAFFIC ASSIGNMENT
Candidate destination nodes for each origin (optional)
Duration of DTA sessions
Duration of simulation “burn time”
Desired number of destination nodes per origin
INCIDENTS
Identify and Schedule of closed lanes
Identify and Schedule of closed links
City of Ashland C‐10 KLD Engineering, P.C.
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Table C‐3. Glossary
Cap The maximum number of vehicles, of a particular movement, that can discharge
from a link within a time interval.
E The number of vehicles, of a particular movement, that enter the link over the
time interval. The portion, ETI, can reach the stop‐bar within the TI.
G/C The green time: cycle time ratio that services the vehicles of a particular turn
movement on a link.
h The mean queue discharge headway, seconds.
k Density in vehicles per lane per mile.
kത The average density of moving vehicles of a particular movement over a TI, on a
link.
L The length of the link in feet.
Lୠ ,Lୣ The queue length in feet of a particular movement, at the [beginning, end] of a
time interval.
LN The number of lanes, expressed as a floating point number, allocated to service a
particular movement on a link.
L୴ The mean effective length of a queued vehicle including the vehicle spacing, feet.
M Metering factor (Multiplier): 1.
Mୠ ,Mୣ
The number of moving vehicles on the link, of a particular movement, that are
moving at the [beginning, end] of the time interval. These vehicles are assumed
to be of equal spacing, over the length of link upstream of the queue.
O The total number of vehicles of a particular movement that are discharged from a
link over a time interval.
O୕ ,O ,O
The components of the vehicles of a particular movement that are discharged
from a link within a time interval: vehicles that were Queued at the beginning of
the TI; vehicles that were Moving within the link at the beginning of the TI;
vehicles that Entered the link during the TI.
P୶ The percentage, expressed as a fraction, of the total flow on the link that
executes a particular turn movement, x.
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Qୠ ,Qୣ The number of queued vehicles on the link, of a particular turn movement, at the
[beginning, end] of the time interval.
Q୫ୟ୶
The maximum flow rate that can be serviced by a link for a particular movement
in the absence of a control device. It is specified by the analyst as an estimate of
link capacity, based upon a field survey, with reference to the HCM.
R The factor that is applied to the capacity of a link to represent the “capacity
drop” when the flow condition moves into the forced flow regime. The lower
capacity at that point is equal to RQ୫ୟ୶ .
RCap The remaining capacity available to service vehicles of a particular movement
after that queue has been completely serviced, within a time interval, expressed
as vehicles.
S୶ Service rate for movement x, vehicles per hour (vph).
tଵ Vehicles of a particular turn movement that enter a link over the first tଵ seconds
of a time interval, can reach the stop‐bar (in the absence of a queue down‐
stream) within the same time interval.
TI The time interval, in seconds, which is used as the simulation time step.
v The mean speed of travel, in feet per second (fps) or miles per hour (mph), of
moving vehicles on the link.
v୕ The mean speed of the last vehicle in a queue that discharges from the link within
the TI. This speed differs from the mean speed of moving vehicles, v.
W The width of the intersection in feet. This is the difference between the link
length which extends from stop‐bar to stop‐bar and the block length.
City of Ashland C‐12 KLD Engineering, P.C.
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Figure C‐1. Representative Analysis Network
8001
8011
3
6
9
12
14
15
16
19
17
2
8107
8
8012
13
22
8009
8010
8005
23 8003
8104
5
10
11
8014
25
24
21
8008
8007
8006
8004
8024
20 8002
Entry, Exit Nodes are
numbered 8xxx
City of Ashland C‐13 KLD Engineering, P.C.
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Figure C‐2. Fundamental Diagrams
Figure C‐3. A UNIT Problem Configuration with t1 > 0
Capacity Drop
Qs
f s
Volume, vph
Qmax
Speed, mph
R vc
k k j c k
Flow Regimes
Density, vpm
Density, vpm
R Qmax
k
vf
Free Forced
L
E2
Me
Qe
Mb
Qb
OE OM OQ
E1 TI
Time
Distance
t1 t2
v
v
vQ
Down
Up
City of Ashland C‐14 KLD Engineering, P.C.
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Figure C‐4. Flow of Simulation Processing (See Glossary: Table C‐3)
Sequence Network Links
Next Time‐step, of duration, TI
Next sweep; Define E, M, S for all
Links
Next Link
Next Turn Movement, x
Get lanes, LNx
Service Rate, Sx; ሺG/Cxሻ
Get inputs to Unit Problem:
Qb ,Mb , E
Solve Unit Problem: Qe ,Me ,O
Last Movement ?
Last Link ?
Last Sweep ?
Calc., store all Link MOE
Set up next TI :
Last Time – step ?
DONE
A
B
C
D
D
C
B
A
No
No
No
No
Yes
Yes
Yes
Yes
APPENDIX D
Detailed Description of Study Procedure
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D. DETAILED DESCRIPTION OF STUDY PROCEDURE
This appendix describes the activities that were performed to compute the Evacuation Time
Estimates. The individual steps of this effort are represented as a flow diagram in Figure D‐1.
Each numbered step in the description that follows corresponds to the numbered element in
the flow diagram.
Step 1
The first activity was to obtain EMZ information and create a GIS base map. The base map
extends beyond the EMZ into the Shadow Region. The EMZ and Shadow Region generally run
northwest to southeast. The study area is situated on OR‐99 and is bounded mostly by I‐5 to
the northeast and Siskiyou Mountain Park to the southwest. The base map incorporates the
local roadway topology, a suitable topographic background and the EMZ boundaries.
Step 2
2010 Census block information was obtained in GIS format. This information was used to
estimate the resident population within the EMZ and the Shadow Region and to define the
spatial distribution and demographic characteristics of the population within the study area.
Employee data were estimated using the U.S. Census Longitudinal Employer‐Household
Dynamics from the OnTheMap Census analysis tool1. Tourist information, schools, medical and
other types of special facilities data were provided by the City of Ashland supplemented with
internet searches.
Step 3
A kickoff meeting was conducted with major stakeholders (city emergency managers, Southern
Oregon University, and transportation and county transit managers). The purpose of the
kickoff meeting was to present an overview of the work effort, identify key agency personnel,
and indicate the data requirements for the study. Specific requests for information were
presented to the city. Unique features of the study area were discussed to identify the local
concerns that should be addressed by the ETE study.
Step 4
Next, a physical survey of the roadway system in the study area was conducted to determine
the geometric properties of the highway sections, the channelization of lanes on each section
of roadway, whether there are any turn restrictions or special treatment of traffic at
intersections, the type and functioning of traffic control devices, gathering signal timings for
pre‐timed traffic signals, and to make the necessary observations needed to estimate realistic
values of roadway capacity.
1https://onthemap.ces.census.gov/
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Step 5
A demographic survey of households within the EMZ was conducted to identify household
dynamics, trip generation characteristics, and evacuation‐related demographic information of
the EMZ population. This information was used to determine important study factors including
the average number of evacuating vehicles used by each household, and the time required to
perform pre‐evacuation mobilization activities.
Step 6
A computerized representation of the physical roadway system, called a link‐node analysis
network, was developed using the UNITES software developed by KLD. Once the geometry of
the network was completed, the network was calibrated using the information gathered during
the road survey (Step 4). Estimates of highway capacity for each link and other link‐specific
characteristics were introduced to the network description. Traffic signal timings were input
accordingly. The link‐node analysis network was imported into a GIS map. Census data was
overlaid in the map, and origin centroids where trips would be generated during the evacuation
process were assigned to appropriate links.
Step 7
Regions (groupings of EMZ) that may be advised to evacuate, were developed.
The need for evacuation can occur over a range of time‐of‐day, day‐of‐week, and season.
Scenarios were developed to capture the variation in evacuation demand, highway capacity and
mobilization time, for different time of day, day of the week, and time of year.
Step 8
Access impaired neighborhoods were identified and classified based on specific criteria. First,
neighborhoods with low population densities were eliminated. Then, neighborhoods with low
wildfire risks were removed. Next, Neighborhoods with limited means of egress were selected.
Last, neighborhood boundaries were refined to eliminate large uninhabited areas. Once access
impaired neighborhoods were identified, recommendations were made for safe refuge areas as
well as evacuation signage for these neighborhoods.
Step 9
The input stream for the DYNEV II model, which integrates the dynamic traffic assignment and
distribution model, DTRAD, with the evacuation simulation model, was created for a prototype
evacuation case – the evacuation of the entire EMZ for a representative scenario.
Step 10
After creating this input stream, the DYNEV II System was executed on the prototype
evacuation case to compute evacuating traffic routing patterns. DYNEV II contains an extensive
suite of data diagnostics which check the completeness and consistency of the input data
specified. The analyst reviews all warning and error messages produced by the model and then
corrects the database to create an input stream that properly executes to completion.
The model assigns destinations to all origin centroids consistent with a (general) radial
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evacuation of the EMZ and Shadow Region, away from the hazard. The analyst may optionally
supplement and/or replace these model‐assigned destinations, based on professional
judgment, after studying the topology of the analysis highway network. The model produces
link and network‐wide measures of effectiveness as well as estimates of evacuation time.
Step 11
The results generated by the prototype evacuation case are critically examined. The
examination includes observing the animated graphics (using the EVAN software which
operates on data produced by DYNEV II) and reviewing the statistics output by the model. This
is a labor‐intensive activity, requiring the direct participation of skilled engineers who possess
the necessary practical experience to interpret the results and to determine the causes of any
problems reflected in the results.
Essentially, the approach is to identify those bottlenecks in the network that represent
locations where congested conditions are pronounced and to identify the cause of this
congestion. This cause can take many forms, either as excess demand due to high rates of trip
generation, improper routing, a shortfall of capacity, or as a quantitative flaw in the way the
physical system was represented in the input stream. This examination leads to one of two
conclusions:
The results are satisfactory; or
The input stream must be modified accordingly.
This decision requires, of course, the application of the user's judgment and experience based
upon the results obtained in previous applications of the model and a comparison of the results
of the latest prototype evacuation case iteration with the previous ones. If the results are
satisfactory in the opinion of the user, then the process continues with Step 14. Otherwise,
proceed to Step 12.
Step 12
There are many "treatments" available to the user in resolving apparent problems. These
treatments range from decisions to reroute the traffic by assigning additional evacuation
destinations for one or more sources, imposing turn restrictions where they can produce
significant improvements in capacity, changing the control treatment at critical intersections so
as to provide improved service for one or more movements, or in prescribing specific
treatments for channelizing the flow so as to expedite the movement of traffic along major
roadway systems. Such "treatments" take the form of modifications to the original prototype
evacuation case input stream. All treatments are designed to improve the representation of
evacuation behavior.
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Step 13
As noted above, the changes to the input stream must be implemented to reflect the
modifications undertaken in Step 12. At the completion of this activity, the process returns to
Step 10 where the DYNEV II System is again executed.
Step 14
Evacuation of transit‐dependent evacuees and special facilities are included in the evacuation
analysis. Fixed routing for transit buses and for school buses, ambulances, and other transit
vehicles are introduced into the final prototype evacuation case data set. DYNEV II generates
route‐specific speeds over time for use in the estimation of evacuation times for the transit
dependent and special facility population groups.
Step 15
The prototype evacuation case was used as the basis for generating all region and scenario‐
specific evacuation cases to be simulated. This process was automated through the UNITES user
interface. For each specific case, the population to be evacuated, the trip generation
distributions, the highway capacity and speeds, and other factors are adjusted to produce a
customized case‐specific data set.
Step 16
All evacuation cases are executed using the DYNEV II System to compute ETE. Once results were
available, quality control procedures were used to assure the results were consistent, dynamic
routing was reasonable, and traffic congestion/bottlenecks were addressed properly.
Step 17
Once vehicular evacuation results are accepted, average travel speeds for transit and special
facility routes were used to compute evacuation time estimates for transit‐dependent
permanent residents, schools, hospitals, and other special facilities.
Step 18
Several ETE sensitivity studies were conducted to consider the impact on ETE based on “what
if” scenarios. These scenarios include direction of wildfire approach and changes to
mobilization time, number of evacuating vehicles per household, number of vehicles evacuating
from the shadow region, and potential implementation of traffic management plans by the City
of Ashland. These scenarios were then compared to the baseline ETE to test if certain tactics
could be used to reduce evacuation time.
Step 19
The simulation results are analyzed, tabulated and graphed. The results were then
documented.
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Figure D‐1. Flow Diagram of Activities
Create GIS Base Map
Gather Census Block and Demographic Data for
Study Area. Project population to 2020.
Field Survey of Roadways within Study Area
Conduct Kickoff Meeting with Stakeholders
Create and Calibrate Link‐Node Analysis Network
Develop Evacuation Regions and Scenarios
Conduct Demographic Survey and Develop Trip
Generation Characteristics
Execute DYNEV II for Prototype Evacuation Case B
A
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Examine Prototype Evacuation Case using EVAN and
DYNEV II Output
Modify Evacuation Destinations and/or Develop
Traffic Control Treatments
A
B
Modify Database to Reflect Changes to Prototype
Evacuation Case
Establish Transit and Special Facility Evacuation
Routes and Update DYNEV‐II Database
Generate DYNEV‐II Input Streams for All Evacuation
Cases
Use DYNEV‐II Average Speed Output to Compute
ETE for Transit and Special Facility Routes
Use DYNEV‐II Results to Estimate Transit and Special
Facilities Evacuation Time Estimates
Conduct “what if” Scenarios
Results Satisfactory
Step 11
Step 12
Step 13
Step 14
Step 15
Step 16
Step 17
Step 18
Identify and Classify Access Impaired
Neighborhoods
Create and Debug DYNEV‐II Input Stream
Step 8
Step 9
Step 10
Documentation
Step 19
APPENDIX E
Facility Data
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E. FACILITY DATA
This appendix lists population information, as of January 2021, for special facilities that are
located within the EMZ that were used in this study. Special facilities are defined as schools,
preschools/daycares, and medical facilities. Tourist population is included in the tables for golf
courses, hiking trails, theatres, and lodging facilities. OnTheMap employment data (see Section
3, sub‐section 3.4) is summarized in the table for major employers. Maps of each school,
preschool/daycare, medical facility, major employer, golf course, hiking trail, theatre, and
lodging facility are also provided.
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Table E‐1. Schools and Preschools/Daycares within the EMZ
EMZ Facility Name Facility Type Street Address Municipality Enrollment
2 Children’s World Preschool/Daycare 175 N Main St Ashland 10
2 Oregon Child Development Preschool/Daycare 265 N Main St Ashland 213
3 Pea Pod Village Preschool/Daycare 518 Auburn St Ashland 10
3 Reflective Hearts Childcare Preschool/Daycare 414 Courtney St Ashland 10
3 Southern Oregon University School 1250 Siskiyou Blvd Ashland 5,000
4 Memory Lane Preschool Preschool/Daycare 1615 Clark Ave Ashland 20
4 Rain and Shine Preschool Preschool/Daycare Harmony Ln Ashland 20
6 Ashland Middle School School 100 Walker Ave Ashland 900
6 Bellview Elementary School School 1070 Tolman Creek Rd Ashland 460
6 John Muir Elementary School School 100 Walker Ave Ashland 270
6 Stone Soup Playschool Preschool/Daycare 782 Park St Ashland 10
6 Walker Elementary School School 364 Walker Ave Ashland 340
7 Ashland High School School 201 S Mountain Ave Ashland 1,000
7 Head Start Preschool/Daycare 421 Walker Ave Ashland 20
10 Helman Elementary School School 705 Helman St Ashland 360
EMZ TOTAL: 8,643
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Table E‐2. Medical Facilities within the EMZ
EMZ Facility Name
Street
Address Municipality
Cap‐
acity
Current
Census
Ambul‐
atory
Patients
Wheel‐
chair
Patients
Bed‐
ridden
Patients
2 Asante Ashland Community Hospital 280 Maple St Ashland 125 125 63 31 31
2 Linda Vista Nursing & Rehab Center 135 Maple St Ashland 100 100 50 25 25
10 Ashland Surgery Center 658 N Main St Ashland 25 25 13 6 6
EMZ TOTAL: 250 250 126 62 62
Table E‐3. Major Employers within the EMZ
EMZ Facility Name Street Address Municipality
Employees
(Max Shift)
Employees
Commuting into
the EMZ
Employee
Vehicles Commuting
into the EMZ
1
Various locations throughout the EMZ1
111 74 70
2 151 100 94
3 938 627 593
4 0 0 0
5 271 181 171
6 711 475 448
7 129 86 81
8 898 599 565
9 81 54 51
10 159 106 100
EMZ TOTAL: 3,449 2,302 2,173
1 The major employer locations identified by the Census Bureau are shown in Figure E-3. The locations are represented by circles which increase in size proportional to the number of
non-EMZ employees present in each Census Block.
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Table E‐4. Recreational Facilities and Lodging Facilities within the EMZ
EMZ Facility Name Facility Type Street Address Municipality Tourists Vehicles
1 Lithia Park Hiking Trail Winburn Way Ashland 1,000 280
2 Acid Castle Boulders Access Point Hiking Trail 183 Hitt Rd Ashland 75 38
3 Ashland Springs Hotel Lodging 212 E Main St Ashland 140 63
3 Columbia Hotel Lodging 262 E Main St Ashland 48 22
3 Oregon Shakespeare Festival Theatre 15 S Pioneer St Ashland 1,975 8862
3 Winchester Inn Lodging 35 S 2nd St Ashland 42 19
4 Oredson Todd Woods Hiking Trail Lupine Dr Ashland 180 90
5 Ashland Hills Hotel & Suites and Convention Center Lodging 2525 Ashland St Ashland 366 164
5 Best Western Windsor Inn Lodging 2520 Ashland St Ashland 186 83
5 Holiday Inn Express & Suites Ashland Lodging 565 Clover Ln Ashland 130 58
5 Oak Knoll Golf Course Golf Course 3070 OR‐66 Ashland 50 50
6 Cedarwood Inn Hotel Lodging 1801 Siskiyou Blvd Ashland 100 45
6 Rodeway Inn Lodging 2359 Ashland St Ashland 96 43
6 Super 8 by Wyndham Ashland Lodging 2350 Ashland St Ashland 132 59
7 Ashland Motel ‐ University Lodging 1145 Siskiyou Blvd Ashland 52 23
7 Flagship Inn‐Ashland Lodging 1193 Siskiyou Blvd Ashland 122 55
7 Palm Hotel Lodging 1065 Siskiyou Blvd Ashland 32 14
7 Timbers Motel Lodging 1450 Ashland St Ashland 60 27
9 North Mountain Park Hiking Trail 620 N Mountain Ave Ashland 180 150
10 Plaza Inn & Suites Lodging 98 Central Ave Ashland 184 83
EMZ TOTAL: 5,150 2,252
2 There is limited parking capacity at Oregon Shakespeare Festival in EMZ 3. According to the city, the vehicles for the festival park at multiple places near the theatre in EMZs 2, 3
and 8. For detailed information, please refer to Section 3.
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Figure E‐1. Schools and Preschools/Daycares within the EMZ
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Figure E‐2. Medical Facilities within the EMZ
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Figure E‐3. Major Employers within the EMZ
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Figure E‐4. Recreational Facilities and Lodging Facilities within the EMZ
APPENDIX F
Demographic Survey
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F. DEMOGRAPHIC SURVEY
This appendix presents the results obtained from a Demographic Survey that was conducted in
support of this study. Outlined below is the survey sampling plan, results obtained, and survey
instrument (See Attachment A).
F.1 Introduction
The development of evacuation time estimates for the City of Ashland Emergency Management
Zones (EMZs) requires the identification of travel patterns, car ownership and household size of
the population. Demographic information can be obtained from Census data, however, the use
of this data has several limitations when applied to emergency planning. First, the Census data
do not encompass the range of information needed to identify the time required for preliminary
activities (mobilization) that must be undertaken prior to evacuating the area. Secondly, Census
data do not contain attitudinal responses needed from the population of the EMZs and
consequently may not accurately represent the anticipated behavioral characteristics of the
evacuating populace.
These concerns are addressed by conducting a demographic survey of a representative sample
of the study area population. The survey is designed to elicit information from the public
concerning family demographics and estimates of response times to well defined events. The
design of the survey includes a limited number of questions of the form “What would you do if
…?” and other questions regarding activities with which the respondent is familiar (“How long
does it take you to …?”).
F.2 Survey Instrument and Sampling Plan
Attachment A presents the final survey instrument used for the demographic survey. A draft of
the instrument was submitted to stakeholders for comment. Comments were received and the
survey instrument was modified accordingly, prior to conducting the survey.
Following the completion of the instrument, a sampling plan was developed. A sample size of
2,471 completed survey forms yields results with a sampling error of approximately ±2.30 at the
99% confidence level. The sample must be drawn from the study population (see Section 3.1)
converted to households using the average household size of 2.06 people per household based
upon 2014‐2018 Census data since the goal is to survey individual households rather than
individual people. A list of zip codes in the study area was developed using geographic
information system (GIS) software.
The demographic survey was conducted through an online form. The demographic survey
primarily advertised utilizing a mass notification system, Nixle. Additionally, the survey was
posted electronically on the city’s websites and Facebook pages.
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F.3 Survey Results
The results of the survey fall into three categories. The first category is household demographic
results. Household demographic information includes such factors as household size, automobile
ownership, automobile availability, commuters, cellphone coverage, and emergency alert and
warning system subscription. The second category of survey results is about evacuation
responses. This section contains results regarding how residents in the study area would respond
to an evacuation. The third category of results contains time distributions for performing certain
pre‐evacuation activities. These data are processed to develop the trip generation distributions
used in the evacuation modeling effort, as discussed in Section 5.
A review of the survey instrument reveals that several questions have a “Don’t Know” (DK) or
“Decline to State” option for a response. It is accepted practice in conducting surveys of this type
to accept the answers of a respondent who offers a DK or “Decline to State” response for a few
questions. To address the issue of occasional DK/declined responses from a large sample, the
practice is to assume that the distribution of these responses is the same as the underlying
distribution of the positive responses. In effect, the DK/declined responses are ignored, and the
distributions are based upon the positive data that is acquired.
F.3.1 Household Demographic Results
Household Size
Figure F‐1 presents the distribution of household size within the EMZs based on the responses to
the demographic survey. The average household contains 2.23 people. The estimated household
size (2.06 persons) used to determine the survey sample was drawn from Census data. The close
agreement (well within the sampling error bounds) between the average household size
obtained from the survey and from the Census is an indication of the reliability of the survey.
Automobile Ownership
The average number of automobiles available per household in the study area is 1.88. The
distribution of automobile ownership is presented in Figure F‐2. It should be noted that only
1.14% of people do not have access to a vehicle. Figure F‐3 and Figure F‐4 present the automobile
availability by household size. As expected, nearly all households of 2 or more people have access
to at least one vehicle. Figure F‐5 shows the percent of households that own an electric vehicle.
Approximately 13 percent of households own at least one electric vehicle.
Ridesharing
An overwhelming proportion (88%) of the households surveyed responded that they could share
a ride with a neighbor, relative, or friend if a car is not available to them when advised to
evacuate.
Functional or Transportation Needs
Approximately 7% of households have a person with functional or transportation needs. Figure
F‐6 shows the breakdown of the percentage of households with each type of need. The majority
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of these people would need a bus to evacuate. It should be noted that about 8.7% of these
people have their own transportation but have a walking or mobility issue/disability.
Commuters
Figure F‐7 presents the distribution of the number of commuters in each household. Commuters
are defined as household members who travel to work or college on a daily basis. The data shows
an average of 0.52 commuters per household in the study area, and approximately 36% of
households have at least one commuter.
Commuter Travel Modes
Figure F‐8 presents the mode of travel that commuters use on a daily basis. The vast majority of
commuters use their private automobiles to travel to work. The data shows an average of 1.06
employees per vehicle, assuming 2 people per vehicle – on average – for carpools.
Commuter Travel Patterns
Figure F‐9 presents the destination area code that commuters travel to on a daily basis. Of the
respondents who commute on a daily basis, approximately 62% work within Ashland (97520 area
code). Of the remaining 38%‐‐12% commute to 97504, 9% commute to 97501 and the remaining
commute to other locations.
F.3.2 Evacuation Response
Several questions were asked to gauge the population’s response to an emergency. These are
now discussed:
“How many vehicles would your household use during a wildfire evacuation?” The response is
shown in Figure F‐1010. On average, evacuating households would use 1.43 vehicles.
“Would your family await the return of other family members prior to evacuating the area?”
Of the survey participants who responded, approximately 39% said they would await the return
of other family members before evacuating and 61% indicated they would not await the return
of other family members.
“What type of pet(s) and/or animal(s) do you have?” Based on the responses, approximately
62% of the households have pets and/or animals. Of the households that own pets and/or
animals, 90.5% of them indicated that they own a domesticated animal (or household pet). This
category includes dogs, cats, birds, reptiles, and fish. Approximately 6.5% of households own
farm animals like horses, chickens, goats, and pigs. Approximately 3% of households indicated
that they own other pets/animals but did not specify.
“If you have a household pet and/or an animal, would you take your pet with you if you were
asked to evacuate the area?” Based on the responses to the survey 98% of households that own
pets and/or animals would take them during an evacuation; the remaining 2% would leave them
at home. Of the respondents who would elect to take their animals with them during an
evacuation, 18% would take them to a shelter, and 80% would take them somewhere else. Of
the households with pets and/or animals, 96% indicated that they have sufficient room in their
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vehicles to evacuate with them. Approximately 2% would use a trailer to evacuate their
pet/animal.
“Emergency officials advise you to take shelter at home in an emergency. Would you?” This
question is designed to elicit information regarding compliance with instructions to shelter‐in‐
place. The results indicate that 94% of households who are advised to shelter‐in‐place would do
so; the remaining 6% would choose to evacuate the area. Therefore, 6% of the population within
the shadow region and within the EMZs not advised to evacuate will voluntarily evacuate.
“Emergency officials advise you to evacuate due to a wildfire. Where would you evacuate to?”
Based on the responses, approximately 39% would evacuate to a friend/relative’s home.
Approximately 29% answered “Don’t Know/Other”. Approximately 24% would evacuate to a
hotel, motel, or campground. Less than one percent would choose not to evacuate. See Figure
F‐11 for complete results.
“Emergency officials advise you to evacuate. Would you notify a neighbor or friend to evacuate
as well?” This question is designed to elicit information regarding notification between residents
in the study area. Approximately 94% of respondents said they would notify a neighbor or a
friend. The remaining 6% said they would not.
“How would you notify a neighbor or friend to evacuate during an emergency?” This question
is designed to see how respondents in the study area would notify neighbors or friends during an
evacuation, if they chose to do so. From the respondents who elected to notify a neighbor or
friend during an evacuation, the majority (39%) would notify in person. From the remaining
respondents, there was a near equal split between using text messages and phone calls at about
29% for each, and 3% of respondents would use some form of social media. Figure F‐12 displays
these results.
“How would you rate the cell phone coverage in your area?” Figure F‐13 presents how the
respondents rated cell phone coverage in their area. The purpose of this question was to gain
insight into how well a cell phone based alert and/or notification would be received. This
question was added for informational purposes only and was not used in this study. As shown in
the figure, the data is more heavily weighted towards good or better with 85% of respondents
rating their cell phone reception in their area as good, very good, or excellent. The remaining
approximately 15% rated cell phone coverage as fair, poor or very poor in their area. (Less than
one percent indicated that they do not have a cell phone available.)
“Have you opted into your local Emergency Alert and Warning systems?” Figure F‐14 displays
the percentages of respondents who have opted into their local emergency alert and warning
systems and by method. The majority of the study area residents who are registered are opted
into either NIXLE or Citizen Alert (99 percent) while a few indicated they are opted into other
emergency alert systems (less than one percent). Of the respondents, 17 percent indicated that
they registered using their residential phone number, 79 percent using their cell phone number,
38 percent using their email address and/or 75 percent opted in by text message. It should be
noted some people are opted into multiple methods of notification.
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F.3.3 Time Distribution Results
The survey asked several questions about the amount of time it takes to perform certain pre‐
evacuation activities. These activities involve actions taken by residents during the course of their
day‐to‐day lives. Thus, the answers fall within the realm of the responder’s experience.
The mobilization distributions provided below are the result of having applied the analysis
described in Section 5.4.1 on the component activities of the mobilization.
“How long would it take you to notify a neighbor or friend to evacuate?” This question is
designed to see how long it would take respondents to notify a neighbor or friend should they
choose to do so. From the respondents who elected to notify a neighbor or friend during an
evacuation, approximately 63% responded they would notify them in 5 minutes or less, 22% said
it would take them between 6 and 10 minutes to notify a neighbor or friend, and 10% said it
would take them between 11 and 15 minutes. The remaining approximately 5% aid it would take
them 20 minutes or more to notify a neighbor or friend during an evacuation. This distribution is
displayed in Figure F‐15.
“How long does it take the commuter to complete preparation for leaving work/college?”
Figure F‐16 presents the cumulative distribution; in all cases, the activity is completed by 60
minutes. Approximately, 90% can leave in less than 30 minutes.
“How long would it take the commuter to travel home?” Figure F‐17 presents the work to home
travel time for the EMZ. Approximately 92% of commuters can arrive home within 30 minutes of
leaving work; all within 60 minutes.
“How long would it take the family to pack clothing, secure the house, and load the car?” Figure
F‐18 presents the time required to prepare for leaving on an evacuation trip. In many ways this
activity mimics a family’s preparation for a short holiday or weekend away from home. Hence,
the responses represent the experience of the responder in performing similar activities.
The distribution shown in Figure F‐18 has a long “tail.” Approximately 90% of households can be
ready to leave home within 105 minutes; the remaining 10% of households require up to an
additional 90 minutes.
F.4 Conclusions
The demographic survey provides valuable, relevant data associated with the study area
population. This data is used to quantify demographics specific to the study area and
“mobilization time”, which can influence evacuation time estimates.
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Figure F‐1. Household Size in the Study Area
Figure F‐2. Vehicle Availability
0%
10%
20%
30%
40%
50%
60%
12345+
Pe
r
c
e
n
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o
f
Ho
u
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o
l
d
s
People
Household Size
0%
10%
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01234+
Pe
r
c
e
n
t
o
f
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u
s
e
h
o
l
d
s
Vehicles
Vehicle Availability
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Figure F‐3. Vehicle Availability ‐ 1 to 4 Person Households
Figure F‐4. Vehicle Availability – 5 to 7+ Person Households
0%
20%
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80%
100%
012345+
Pe
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o
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Vehicles
Distribution of Vehicles by HH Size
1‐5 Person Households
1 Person 2 People 3 People 4 People 5 People
0%
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80%
100%
012345+
Pe
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t
o
f
Ho
u
s
e
h
o
l
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Vehicles
Distribution of Vehicles by HH Size
5‐9+ Person Households
6 People 7 People 8 People 9+ People
City of Ashland F‐8 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐5. Electric Vehicle Ownership
Figure F‐6. Functional or Transportation Needs
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ZERO (NONE)ONE TWO +
Pe
r
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n
t
o
f
Ho
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e
h
o
l
d
s
Electric Cars
0%
10%
20%
30%
40%
50%
Bus Medical Bus/Van Wheelchair
Accessible Vehicle
Ambulance Walking/Mobility
Issues/Disability
(has vehicle)
Pe
r
c
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n
t
o
f
Ho
u
s
e
h
o
l
d
s
wi
t
h
Fu
n
c
t
i
o
n
a
l
or
Tr
a
n
s
p
o
r
t
a
t
i
o
n
Ne
e
d
s
Type of Need
Functional or Transportation Needs
City of Ashland F‐9 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐7. Commuters in Households in the Study Area
Figure F‐8. Modes of Travel in the Study Area
0%
10%
20%
30%
40%
50%
60%
70%
01234+
Pe
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n
t
o
f
Ho
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h
o
l
d
s
Commuters
Commuters Per Household
0%
20%
40%
60%
80%
100%
Bus Walk/Bike Drive Alone Carpool (2+)
Pe
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n
t
of
Co
m
m
u
t
e
r
s
Mode of Travel
Travel Mode to Work
City of Ashland F‐10 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐9. Commuter Travel Patterns
Figure F‐10. Number of Vehicles Used for Evacuation
0%
20%
40%
60%
80%
Pe
r
c
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n
t
of
Co
m
m
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t
e
r
s
Zip Code
Commuter Travel Patterns
0%
20%
40%
60%
80%
100%
01234+
Pe
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t
of
Ho
u
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e
h
o
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s
Vehicles
Evacuating Vehicles Per Household
City of Ashland F‐11 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐11. Study Area Shelter Locations
Figure F‐12. Method to Notify a Friend/Neighbor
0%
10%
20%
30%
40%
Friend/Relative
Home
Reception
Center
A Hotel, Motel,
or Campground
A
Second/Seasonal
Home
Would not
Evacuate
Don't
Know/Other
Pe
r
c
e
n
t
o
f
Ho
u
s
e
h
o
l
d
s
Shelter Locations
0%
20%
40%
TEXT MESSAGE PHONE CALL SOCIAL MEDIA IN PERSON
Pe
r
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n
t
o
f
Ho
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s
e
h
o
l
d
s
Method to Notify a Friend/Neighbor
City of Ashland F‐12 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐13. Cell Phone Coverage
Figure F‐14. Emergency Alert Opt‐in Method by System
0%
20%
40%
EXCELLENT VERY GOOD GOOD FAIR POOR VERY POOR DON'T HAVE
A CELL
PHONE
Pe
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n
t
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f
Ho
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e
h
o
l
d
s
Cell Phone Coverage
0%
5%
10%
15%
20%
25%
30%
With Residential Phone With Cellular Phone With Email With Text Message
Pe
r
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n
t
of
Ho
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e
h
o
l
d
s
Emergency Alert Opt‐in Method by System
OPTED IN CITIZEN ALERT OPTED IN NIXLE BOTH CITIZEN ALERT AND NIXLE OTHER SYSTEMS
City of Ashland F‐13 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐15. Notification Time
Figure F‐16. Time Required to Prepare to Leave Work/College
0%
20%
40%
60%
80%
100%
0 1020304050
Pe
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f
Ho
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h
o
l
d
s
Preparation Time (min)
Notification Time
0%
20%
40%
60%
80%
100%
0 10203040506070
Pe
r
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n
t
of
Co
m
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t
e
r
s
Preparation Time (min)
Time to Prepare to Leave Work/College
City of Ashland F‐14 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure F‐17. Time to Travel Home from Work/College
Figure F‐18. Time to Prepare Home for Evacuation
0%
20%
40%
60%
80%
100%
0 10203040506070
Pe
r
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n
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o
f
Co
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t
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r
s
Travel Time (min)
Work to Home Travel
0%
20%
40%
60%
80%
100%
0 60 120 180 240
Pe
r
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t
o
f
Ho
u
s
e
h
o
l
d
s
Preparation Time (min)
Preparation Time with Everyone Home
City of Ashland F‐15 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
ATTACHMENT A
Demographic Survey Instrument
City of Ashland F‐16 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
City of Ashland Wildfire Egress Study
* Required
Purpose
The City of Ashland is currently undertaking an evacuation study to determine how long it would take to evacuate
the City under different circumstances (weekday versus weekend, midday versus evening, etc.). The results of this
study will be used to enhance emergency response plans for the City and help protect our residents and visitors.
The survey below includes questions designed to estimate demographics for the City that are not available from
the U.S. Census Bureau. These demographics help determine the number of vehicles that will be evacuating the
City in the event of an emergency. The survey also includes questions designed to estimate the time needed by
residents and visitors to prepare to evacuate.
Please do not provide your name or any personal information, and the survey should take less than 5 minutes to
complete.
1. What is your home zip code? *
Mark only one oval.
97520
97530
97535
97501
97502
97503
97504
Prefer not to say
Other:
City of Ashland F‐17 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
2A. In total, how many running cars, or other vehicles are usually available to the
household?
Mark only one oval.
ONE
TWO
THREE
FOUR
FIVE
SIX
SEVEN
EIGHT
NINE OR MORE
ZERO (NONE)
PREFER NOT TO SAY
2B. Of these running cars, or other vehicles, how many of them are powered by
electric?
Mark only one oval.
ONE
TWO
THREE
FOUR
FIVE
SIX
SEVEN
EIGHT
NINE OR MORE
ZERO (NONE)
PREFER NOT TO SAY
City of Ashland F‐18 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
2C. If you didn't have a car, would you be able to get a ride out of the area with a
neighbor or friend?
Mark only one oval.
YES
NO
YES, IF THEY WERE HOME
UNSURE
PREFER NOT TO SAY
3. Please specify the number of people in your household who require Functional or
Transportation needs (public/private transportation assistance ‐ bus ‐ or specialized
vehicle due to a medical condition ‐ wheelchair transport or ambulance) in an
evacuation:
Mark only one oval per row.
0 1 2 3 4 More
than 4
If Other for Question 3, Please Specify:
Bus
Medical Bus/Van
Wheelchair Accessible Vehicle
Ambulance
Other
City of Ashland F‐19 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
4. How many vehicles would your household use during a wildfire evacuation?
Mark only one oval.
0 (NONE)
1
2
3
4
5
6
7
8
9 OR MORE
I WOULD EVACUATE BY BICYCLE
I WOULD EVACUATE BY BUS
PREFER NOT TO SAY
City of Ashland F‐20 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
5. How many people usually live in this household?
Mark only one oval.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19 OR MORE
PREFER NOT TO SAY
City of Ashland F‐21 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
City of Ashland F‐22 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
8‐2. What zip code does Commuter #2 commute to for work or college?
8‐3. What zip code does Commuter #3 commute to for work or college?
8‐4. What zip code does Commuter #4 commute to for work or college?
City of Ashland F‐23 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
9‐1. How much time on average, would it take Commuter #1 to travel home from work or
college?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 9‐1, Please Specify:
City of Ashland F‐24 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
9‐2. How much time on average, would it take Commuter #2 to travel home from work or
college?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 9‐2, Please Specify:
City of Ashland F‐25 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
9‐3. How much time on average, would it take Commuter #3 to travel home from work or
college?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 9‐3, Please Specify:
City of Ashland F‐26 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
9‐4. How much time on average, would it take Commuter #4 to travel home from work or
college?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 9‐4, Please Specify:
City of Ashland F‐27 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
10‐1. A wildfire is impacting the area where you live, work or go to college, approximately
how much time would it take Commuter #1 to complete preparation for leaving work or
college prior to starting the trip home?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 10‐1, Please Specify:
City of Ashland F‐28 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
10‐2. A wildfire is impacting the area where you live, work or go to college, approximately
how much time would it take Commuter #2 to complete preparation for leaving work or
college prior to starting the trip home?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 10‐2, Please Specify:
City of Ashland F‐29 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
10‐3. A wildfire is impacting the area where you live, work or go to college, approximately
how much time would it take Commuter #3 to complete preparation for leaving work or
college prior to starting the trip home?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
If Over 2 Hours for Question 10‐3, Please Specify:
City of Ashland F‐30 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
10‐4. A wildfire is impacting the area where you live, work or go to college,
approximately how much time would it take Commuter #4 to complete preparation for
leaving work or college prior to starting the trip home?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR, BUT LESS THAN 1 HOUR 15 MINUTES
BETWEEN 1 HOUR 16 MINUTES AND 1 HOUR 30 MINUTES
BETWEEN 1 HOUR 31 MINUTES AND 1 HOUR 45 MINUTES
BETWEEN 1 HOUR 46 MINUTES AND 2 HOURS
OVER 2 HOURS
PREFER NOT TO SAY
Other:
30. If Over 2 Hours for Question 10‐4, Please Specify:
City of Ashland F‐31 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
11. If you were advised by local authorities to evacuate due to a wildfire, how much
time would it take the household to pack clothing, medications, secure the house,
load the car, and complete preparations prior to evacuating the area?
Mark only one oval.
LESS THAN 15 MINUTES
15-30 MINUTES
31-45 MINUTES
46 MINUTES - 1 HOUR
1 HOUR TO 1 HOUR 15 MINUTES
1 HOUR 16 MINUTES TO 1 HOUR 30 MINUTES
1 HOUR 31 MINUTES TO 1 HOUR 45 MINUTES
1 HOUR 46 MINUTES TO 2 HOURS
2 HOURS TO 2 HOURS 15 MINUTES
2 HOURS 16 MINUTES TO 2 HOURS 30 MINUTES
2 HOURS 31 MINUTES TO 2 HOURS 45 MINUTES
2 HOURS 46 MINUTES TO 3 HOURS
3 HOURS TO 3 HOURS 15 MINUTES
3 HOURS 16 MINUTES TO 3 HOURS 30 MINUTES
3 HOURS 31 MINUTES TO 3 HOURS 45 MINUTES
3 HOURS 46 MINUTES TO 4 HOURS
4 HOURS TO 4 HOURS 15 MINUTES
4 HOURS 16 MINUTES TO 4 HOURS 30 MINUTES
4 HOURS 31 MINUTES TO 4 HOURS 45 MINUTES
4 HOURS 46 MINUTES TO 5 HOURS
5 HOURS TO 5 HOURS 30 MINUTES
5 HOURS 31 MINUTES TO 6 HOURS
OVER 6 HOURS
WILL NOT EVACUATE
PREFER NOT TO SAY
Other:
City of Ashland F‐32 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
If Over 6 Hours for Question 11, Please Specify:
12. Please choose one of the following:
If you do not know what you would do, or you prefer not to answer, please leave this question blank.
Mark only one oval.
During a wildfire situation, I would await the return of household members to evacuate
together.
During a wildfire situation, I would evacuate independently and meet other household
members later.
13A. Emergency officials advise you to not evacuate in a wildfire emergency
because you are not in the area of risk. Would you:
Mark only one oval.
NOT EVACUATE
EVACUATE
DON'T KNOW/PREFER NOT TO SAY
City of Ashland F‐33 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
evacuate to?
Mark only one oval.
A RELATIVE’S OR FRIEND’S HOME
A RECEPTION CENTER
A HOTEL, MOTEL OR CAMPGROUND
A SECOND/SEASONAL HOME
WOULD NOT EVACUATE
DON'T KNOW
OTHER (Specify Below)
PREFER NOT TO SAY
Fill in OTHER answers for 13B
14A. Do you have any pet(s) and/or animal(s)? *
Mark only one oval.
YES
NO
PREFER NOT TO SAY
City of Ashland F‐34 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
14B. What type of pet(s) and/or animal(s) do you have?
Check all that apply.
DOG
CAT
BIRD
REPTILE
HORSE
FISH
CHICKEN
GOAT
PIG
OTHER SMALL PETS/ANIMALS (Specify Below)
OTHER LARGE PETS/ ANIMALS (Specify Below)
Other:
Mark only one oval.
PREFER NOT TO SAY
14C. What would you do with your pet(s) and/or animal(s) if you had to evacuate?
Mark only one oval.
TAKE PET WITH ME TO A SHELTER
TAKE PET WITH ME SOMEWHERE ELSE
LEAVE PET AT HOME
PREFER NOT TO SAY
City of Ashland F‐35 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
14D. Do you have sufficient room in your vehicle(s) to evacuate with your pet(s)
and/or animal(s)?
Mark only one oval.
YES
NO
WILL USE A TRAILER
PREFER NOT TO SAY
Other:
15. How would you rate the cell phone coverage in your area?
Mark only one oval.
EXCELLENT
VERY GOOD
GOOD
FAIR
POOR
VERY POOR
DON'T HAVE A CELL PHONE
PREFER NOT TO SAY
City of Ashland F‐36 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
City of Ashland F‐37 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
17B. How would you notify your neighbor or friend to evacuate?
Check all that apply.
TEXT MESSAGE
PHONE CALL
SOCIAL MEDIA
IN PERSON
PREFER NOT TO SAY
Other:
17C. How long would it take you to notify your neighbor or friend to evacuate?
Mark only one oval.
5 MINUTES OR LESS
6-10 MINUTES
11-15 MINUTES
16-20 MINUTES
21-25 MINUTES
26-30 MINUTES
31-35 MINUTES
36-40 MINUTES
41-45 MINUTES
46-50 MINUTES
51-55 MINUTES
56 - 1 HOUR
OVER 1 HOUR
PREFER NOT TO SAY
APPENDIX G
Evacuation Regions
City of Ashland G‐1 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
G EVACUATION REGIONS
This appendix presents the evacuation percentages for each Evacuation Region (Table G‐1) and
maps of all Evacuation Regions. The shelter‐in‐place percentages presented in Table G‐1 are
based on the methodology discussed in assumption 3 of Section 2.3 and the results of the
demographic survey.
The evacuation regions were created based on the City of Ashland Evacuation Management
Zones (EMZs). Regions R01 through R10 represent evacuations of each individual EMZ by itself.
Regions R11 through R17 are evacuations of combinations of EMZ based on the origin of a
potential wildfire and prevailing winds. Lastly, Region R18 is the evacuation of all EMZs at once.
City of Ashland G‐2 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Table G‐1. Percent of EMZ Population Evacuating for Each Region
Region
Emergency Management Zone (EMZ)
Description 1 2 3 4 5 6 7 8 9 10
R01 EMZ 1 100% 6% 6% 6% 6% 6% 6% 6% 6% 6%
R02 EMZ 2 6% 100% 6% 6% 6% 6% 6% 6% 6% 6%
R03 EMZ 3 6% 6% 100% 6% 6% 6% 6% 6% 6% 6%
R04 EMZ 4 6% 6% 6% 100% 6% 6% 6% 6% 6% 6%
R05 EMZ 5 6% 6% 6% 6% 100% 6% 6% 6% 6% 6%
R06 EMZ 6 6% 6% 6% 6% 6% 100% 6% 6% 6% 6%
R07 EMZ 7 6% 6% 6% 6% 6% 6% 100% 6% 6% 6%
R08 EMZ 8 6% 6% 6% 6% 6% 6% 6% 100% 6% 6%
R09 EMZ 9 6% 6% 6% 6% 6% 6% 6% 6% 100% 6%
R10 EMZ 10 6% 6% 6% 6% 6% 6% 6% 6% 6% 100%
R11
Western Ashland
‐ EMZ1, EMZ2,
EMZ3, EMZ4
100% 100% 100% 100% 6% 6% 6% 6% 6% 6%
R12
Eastern Ashland ‐
EMZ5, EMZ6,
EMZ7, EMZ8,
EMZ9, EMZ10
6% 6% 6% 6% 100% 100% 100% 100% 100% 100%
R13
Northern Ashland
‐ EMZ1, EMZ2,
EMZ10
100% 100% 6% 6% 6% 6% 6% 6% 6% 100%
R14
Central Ashland ‐
EMZ3, EMZ7,
EMZ8, EMZ9
6% 6% 100% 6% 6% 6% 100% 100% 100% 6%
R15
Southern Ashland
‐ EMZ4, EMZ5,
EMZ6
6% 6% 6% 100% 100% 100% 6% 6% 6% 6%
R16
Northern and
Central Ashland ‐
EMZ1, EMZ2,
EMZ3, EMZ7,
EMZ8, EMZ9,
EMZ10
100% 100% 100% 6% 6% 6% 100% 100% 100% 100%
R17
Southern and
Central Ashland ‐
EMZ3, EMZ4,
EMZ5, EMZ6,
EMZ7, EMZ8,
EMZ9
6% 6% 100% 100% 100% 100% 100% 100% 100% 6%
R18 All EMZs 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
EMZ(s) Shelter‐in‐Place EMZ(s) Evacuate
City of Ashland G‐3 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐1. Region R01
City of Ashland G‐4 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐2. Region R02
City of Ashland G‐5 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐3. Region R03
City of Ashland G‐6 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐4. Region R04
City of Ashland G‐7 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐5. Region R05
City of Ashland G‐8 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐6. Region R06
City of Ashland G‐9 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐7. Region R07
City of Ashland G‐10 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐8. Region R08
City of Ashland G‐11 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐9. Region R09
City of Ashland G‐12 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐10. Region R10
City of Ashland G‐13 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐11. Region R11
City of Ashland G‐14 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐12. Region R12
City of Ashland G‐15 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐13. Region R13
City of Ashland G‐16 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐14. Region R14
City of Ashland G‐17 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐15. Region R15
City of Ashland G‐18 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐16. Region R16
City of Ashland G‐19 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐17. Region R17
City of Ashland G‐20 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Figure G‐18. Region R18
APPENDIX H
Evacuation Roadway Network
City of Ashland H‐1 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
H. EVACUATION ROADWAY NETWORK
This appendix presents the evacuation roadway network used in the study. As discussed in
Section 1.3, a link‐node analysis network was constructed to model the roadway network
within the study area. Figure H‐1 provides an overview of the link‐node analysis network. The
figure has been divided up into 14 more detailed figures (Figure H‐2 through Figure H‐15) which
show each of the links and nodes in the network.
The analysis network was calibrated using the observations made during the field survey
conducted in July 2020. Table H‐1 lists the characteristics of each roadway section modeled in
the study area. Each link is identified by its road name and the upstream and downstream node
numbers. The geographic location of each link can be observed by referencing the grid map
number provided in Table H‐1. The roadway type identified in Table H‐1 is generally based on
the following criteria:
Freeway: limited access highway, 2 or more lanes in each direction, high free flow
speeds
Freeway ramp: ramp on to or off of a limited access highway
Major arterial: 3 or more lanes in each direction
Minor arterial: 2 or more lanes in each direction
Collector: single lane in each direction
Local roadways: single lane in each direction, local roads with low free flow speeds
The term, “No. of Lanes” in Table H‐1 identifies the number of lanes that extend throughout the
length of the link. Many links have additional lanes on the immediate approach to an
intersection (turn pockets); these have been recorded and entered into the input stream for the
DYNEV II System.
As discussed in Section 1.3, lane width and shoulder width were not physically measured during
the road survey. Rather, estimates of these measures were based on visual observations and
recorded images.
Table H‐2 identifies each node in the network that is controlled and the type of control (stop
sign, yield sign, pre‐timed signal, actuated signal, traffic control point) at that node.
Uncontrolled nodes are not included in Table H‐2. The location of each node can be observed
by referencing the grid map number provided.
City of Ashland H‐2 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Table H‐1. Evacuation Roadway Network Characteristics
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
1 1 67 OR‐99 MINOR ARTERIAL 1,080 2 12 4 1,900 45 1
2 1 190 COLVER RD LOCAL ROADWAY 196 1 12 4 1,700 40 1
3 1 222 SUNCREST RD COLLECTOR 489 1 12 4 900 20 1
4 2 38 OR‐99 COLLECTOR 2,116 1 12 4 1,700 45 13
5 2 336 CROWSON RD LOCAL ROADWAY 2,355 1 12 4 1,700 40 14
6 3 4 I‐5 FREEWAY 2,440 2 12 8 2,250 70 13
7 3 10 I‐5 FREEWAY 2,689 2 12 8 2,250 70 14
8 4 3 I‐5 FREEWAY 2,440 2 12 8 2,250 70 13
9 4 5 I‐5 FREEWAY 1,833 2 12 8 2,250 70 11
10 4 8 I‐5 FREEWAY RAMP FREEWAY RAMP 747 2 12 4 1,750 45 11
11 5 4 I‐5 FREEWAY 1,833 2 12 8 2,250 70 11
12 5 6 I‐5 FREEWAY 2,471 2 12 8 2,250 70 11
13 5 7 I‐5 FREEWAY RAMP FREEWAY RAMP 933 1 12 4 1,750 45 11
14 6 5 I‐5 FREEWAY 2,471 2 12 8 2,250 70 11
15 6 14 I‐5 FREEWAY 1,206 2 12 8 2,250 70 11
16 7 4 I‐5 FREEWAY RAMP FREEWAY RAMP 986 1 12 4 1,700 45 11
17 7 8 OR‐66 COLLECTOR 575 1 12 4 1,750 30 11
18 8 5 I‐5 FREEWAY RAMP FREEWAY RAMP 1,151 1 12 4 1,700 45 11
19 8 7 OR‐66 COLLECTOR 575 1 12 4 1,750 30 11
20 8 74 ASHLAND ST COLLECTOR 844 1 12 4 1,575 35 11
21 9 7 OR‐66 LOCAL ROADWAY 1,131 2 12 4 1,750 30 11
22 9 306 TOLMAN CREEK ROAD COLLECTOR 653 1 12 4 1,350 30 11
23 9 307 TOLMAN CREEK ROAD COLLECTOR 1,160 1 12 4 1,350 30 11
24 10 3 I‐5 FREEWAY 2,689 2 12 8 2,250 70 14
25 10 11 I‐5 FREEWAY 2,764 2 12 8 2,250 70 14
26 11 10 I‐5 FREEWAY 2,764 2 12 8 2,250 70 14
27 11 12 I‐5 FREEWAY 2,608 2 12 8 2,250 70 14
28 12 11 I‐5 FREEWAY 2,608 2 12 8 2,250 70 14
29 12 13 I‐5 FREEWAY 2,196 2 12 8 2,250 70 14
30 13 12 I‐5 FREEWAY 2,196 2 12 8 2,250 70 14
City of Ashland H‐3 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
32 14 6 I‐5 FREEWAY 1,206 2 12 8 2,250 70 11
33 14 15 I‐5 FREEWAY 2,680 2 12 8 2,250 70 11
34 15 14 I‐5 FREEWAY 2,680 2 12 8 2,250 70 11
35 15 16 I‐5 FREEWAY 2,027 2 12 8 2,250 70 8
36 16 15 I‐5 FREEWAY 2,027 2 12 8 2,250 70 8
37 16 17 I‐5 FREEWAY 3,454 2 12 8 2,250 70 8
38 17 16 I‐5 FREEWAY 3,454 2 12 8 2,250 70 8
39 17 18 I‐5 FREEWAY 507 2 12 8 2,250 70 8
40 18 17 I‐5 FREEWAY 507 2 12 8 2,250 70 8
41 18 19 I‐5 FREEWAY 2,260 2 12 8 2,250 70 7
42 19 18 I‐5 FREEWAY 2,260 2 12 8 2,250 70 7
43 19 20 I‐5 FREEWAY 984 2 12 8 2,250 70 5
44 20 19 I‐5 FREEWAY 982 2 12 8 2,250 70 5
45 20 21 I‐5 FREEWAY 2,397 2 12 8 2,250 70 5
46 21 20 I‐5 FREEWAY 2,397 2 12 8 2,250 70 5
47 21 22 I‐5 FREEWAY 2,120 2 12 8 2,250 70 5
48 22 21 I‐5 FREEWAY 2,120 2 12 8 2,250 70 5
49 22 23 I‐5 FREEWAY 2,850 2 12 8 2,250 70 5
50 23 22 I‐5 FREEWAY 2,850 2 12 8 2,250 70 5
51 23 24 I‐5 FREEWAY 1,270 2 12 8 2,250 70 5
52 24 23 I‐5 FREEWAY 1,270 2 12 8 2,250 70 5
53 24 25 I‐5 FREEWAY 1,855 2 12 8 2,250 70 4
54 24 73 I‐5 FREEWAY RAMP FREEWAY RAMP 1,159 1 12 4 1,750 45 5
55 25 24 I‐5 FREEWAY 1,854 2 12 8 2,250 70 4
56 25 26 I‐5 FREEWAY 2,912 2 12 8 2,250 70 4
57 25 72 I‐5 FREEWAY RAMP FREEWAY RAMP 1,351 1 12 4 1,700 30 4
58 26 25 I‐5 FREEWAY 2,912 2 12 8 2,250 70 4
59 26 27 I‐5 FREEWAY 3,029 2 12 8 2,250 70 4
60 27 26 I‐5 FREEWAY 3,029 2 12 8 2,250 70 4
61 27 28 I‐5 FREEWAY 2,724 2 12 8 2,250 70 4
62 28 27 I‐5 FREEWAY 2,724 2 12 8 2,250 70 4
63 28 29 I‐5 FREEWAY 1,599 2 12 8 2,250 70 4
City of Ashland H‐4 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
64 28 91 I‐5 FREEWAY RAMP FREEWAY RAMP 580 1 12 4 1,700 45 4
65 29 28 I‐5 FREEWAY 1,598 2 12 8 2,250 70 4
66 29 30 I‐5 FREEWAY 2,020 2 12 8 2,250 70 2
67 29 90 I‐5 FREEWAY RAMP FREEWAY RAMP 290 1 12 4 1,700 45 4
68 30 29 I‐5 FREEWAY 2,020 2 12 8 2,250 70 2
69 30 31 I‐5 FREEWAY 2,656 2 12 8 2,250 70 1
70 31 30 I‐5 FREEWAY 2,656 2 12 8 2,250 70 1
71 32 13 OR‐99 FREEWAY RAMP 1,964 1 12 4 1,700 45 14
72 34 32 OR‐99 COLLECTOR 899 1 12 4 1,700 45 14
73 35 34 OR‐99 COLLECTOR 983 1 12 4 1,700 45 14
74 38 35 OR‐99 COLLECTOR 2,708 1 12 4 1,700 45 14
75 40 2 OR‐99 COLLECTOR 2,806 1 12 4 1,750 25 13
76 40 308 TOLMAN CREEK ROAD COLLECTOR 1,357 1 12 4 1,350 30 13
77 41 127 S MOUNTAIN AVE COLLECTOR 451 1 12 4 1,125 25 10
78 41 233 OR‐99 COLLECTOR 812 2 12 4 1,750 30 9
79 41 339 OR‐99 COLLECTOR 541 2 12 4 1,900 30 10
80 42 233 OR‐99 COLLECTOR 480 2 12 4 1,750 30 9
81 42 237 OR‐99 COLLECTOR 680 2 12 4 1,900 30 9
82 43 44 OR‐99 COLLECTOR 205 2 12 4 1,750 30 9
83 43 240 OR‐99 COLLECTOR 676 2 12 4 1,750 30 9
84 44 96 E MAIN ST COLLECTOR 1,675 1 12 4 1,350 30 9
85 44 148 LITHIA WAY MINOR ARTERIAL 338 2 12 4 1,750 20 9
86 45 227 LITHIA WAY MINOR ARTERIAL 477 2 12 4 1,750 20 9
87 46 254 LITHIA WAY MINOR ARTERIAL 304 2 12 4 1,900 20 9
88 46 256 OAK ST LOCAL ROADWAY 343 1 12 4 1,125 25 9
89 47 241 OR‐99 COLLECTOR 501 2 12 4 1,900 30 7
90 48 49 OR‐99 COLLECTOR 1,114 1 12 4 1,350 30 7
91 49 50 OR‐99 COLLECTOR 643 1 12 4 1,350 30 6
92 50 51 OR‐99 COLLECTOR 488 1 12 8 1,350 30 6
93 51 52 OR‐99 COLLECTOR 942 1 12 8 1,575 35 6
94 52 159 OR‐99 COLLECTOR 311 1 12 4 1,700 40 6
95 53 54 OR‐99 COLLECTOR 986 1 12 4 1,700 40 6
City of Ashland H‐5 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
96 54 55 OR‐99 COLLECTOR 319 1 12 4 1,700 40 6
97 55 56 OR‐99 COLLECTOR 1,007 1 12 8 1,700 50 6
98 56 57 OR‐99 COLLECTOR 906 1 12 4 1,700 50 5
99 57 58 OR‐99 COLLECTOR 804 1 12 4 1,700 50 5
100 58 242 OR‐99 COLLECTOR 687 2 12 4 1,750 50 5
101 59 179 S VALLEY VIEW RD COLLECTOR 737 1 12 4 1,575 35 4
102 59 244 OR‐99 COLLECTOR 2,298 2 12 8 1,900 50 4
103 60 61 OR‐99 COLLECTOR 3,013 1 12 8 1,700 55 4
104 60 180 TALENT AVE LOCAL ROADWAY 790 1 12 4 1,125 25 4
105 61 62 OR‐99 COLLECTOR 1,892 1 12 8 1,700 55 4
106 62 63 OR‐99 COLLECTOR 2,316 1 12 4 1,700 45 4
107 62 185 CREEL RD LOCAL ROADWAY 1,270 1 12 4 1,125 25 4
108 63 186 ARNOS LOCAL ROADWAY 954 1 12 4 1,125 25 4
109 63 245 OR‐99 COLLECTOR 1,065 1 12 4 1,700 45 4
110 64 65 OR‐99 MINOR ARTERIAL 2,114 2 12 4 1,750 45 3
111 65 193 W VALLEY VIEW RD LOCAL ROADWAY 1,152 2 12 4 1,900 40 3
112 65 294 OR‐99 MINOR ARTERIAL 727 2 12 4 1,900 45 3
113 66 1 OR‐99 MINOR ARTERIAL 584 2 12 4 1,750 45 1
114 67 68 OR‐99 MINOR ARTERIAL 1,318 2 12 4 1,900 45 1
115 68 69 OR‐99 MINOR ARTERIAL 2,396 1 12 4 1,275 45 1
116 70 108 OR‐66 MINOR ARTERIAL 974 2 12 4 1,750 30 10
117 70 109 OR‐99 COLLECTOR 1,176 2 12 4 1,750 45 10
118 70 123 OR‐99 COLLECTOR 273 2 12 4 1,750 30 10
119 72 24 I‐5 FREEWAY RAMP FREEWAY RAMP 627 1 12 4 1,700 45 5
120 72 73 S VALLEY VIEW RD COLLECTOR 851 1 12 4 1,575 35 4
121 73 25 I‐5 FREEWAY RAMP FREEWAY RAMP 792 1 12 4 1,700 30 4
122 74 8 ASHLAND ST COLLECTOR 841 1 12 4 1,750 35 11
123 74 75 ASHLAND ST COLLECTOR 567 1 12 4 1,575 35 11
124 75 74 ASHLAND ST COLLECTOR 572 1 12 4 1,575 35 11
125 75 332 ASHLAND ST COLLECTOR 431 1 12 4 1,575 35 12
126 77 78 GREEN SPRINGS HWY COLLECTOR 496 1 12 4 1,575 35 12
127 77 332 ASHLAND ST COLLECTOR 164 1 12 4 1,575 35 12
City of Ashland H‐6 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
128 78 79 GREEN SPRINGS HWY COLLECTOR 739 1 12 4 1,575 35 14
129 79 80 GREEN SPRINGS HWY COLLECTOR 732 1 12 4 1,575 35 14
130 80 81 GREEN SPRINGS HWY COLLECTOR 1,218 1 12 4 1,575 35 14
131 81 82 GREEN SPRINGS HWY COLLECTOR 491 1 12 4 1,700 35 14
132 82 83 GREEN SPRINGS HWY COLLECTOR 2,563 1 12 4 1,575 35 14
133 82 115 CROWSON RD LOCAL ROADWAY 758 1 12 4 1,700 40 14
134 83 84 GREEN SPRINGS HWY COLLECTOR 1,163 1 12 4 1,575 35 14
135 84 85 GREEN SPRINGS HWY COLLECTOR 1,832 1 12 4 1,575 35 14
136 85 86 GREEN SPRINGS HWY COLLECTOR 939 1 12 4 1,575 35 14
137 86 87 GREEN SPRINGS HWY COLLECTOR 2,326 1 12 4 1,575 35 14
138 87 88 GREEN SPRINGS HWY COLLECTOR 1,861 1 12 4 1,575 35 14
140 90 28 I‐5 FREEWAY RAMP FREEWAY RAMP 1,652 1 12 4 1,700 45 4
141 90 91 W VALLEY VIEW RD LOCAL ROADWAY 1,337 1 12 4 1,350 30 4
142 91 29 I‐5 FREEWAY RAMP FREEWAY RAMP 1,125 1 12 4 1,700 45 4
143 91 90 W VALLEY VIEW RD LOCAL ROADWAY 1,334 1 12 4 1,350 30 4
144 91 205 W VALLEY VIEW RD LOCAL ROADWAY 480 1 12 4 1,350 30 4
145 92 93 COLVER RD LOCAL ROADWAY 738 1 12 4 1,700 45 1
146 93 94 COLVER RD LOCAL ROADWAY 1,603 1 12 4 1,700 50 1
147 94 95 COLVER RD LOCAL ROADWAY 444 1 12 4 900 50 1
148 96 44 E MAIN ST COLLECTOR 1,676 1 12 4 1,750 30 9
149 96 97 E MAIN ST COLLECTOR 1,034 1 12 4 1,750 30 9
150 97 96 E MAIN ST COLLECTOR 1,034 1 12 4 1,350 30 9
151 97 98 E MAIN ST COLLECTOR 1,770 1 12 4 1,350 30 10
152 97 127 S MOUNTAIN AVE COLLECTOR 1,298 1 12 4 1,125 25 10
153 98 97 E MAIN ST COLLECTOR 1,769 1 12 4 1,750 30 10
154 98 112 E MAIN ST COLLECTOR 1,230 1 12 4 1,350 30 10
155 98 249 WIGHTMAN ST COLLECTOR 528 1 12 0 1,125 25 10
156 99 100 E MAIN ST COLLECTOR 584 1 12 4 1,350 30 10
157 100 101 E MAIN ST COLLECTOR 1,477 1 12 4 1,350 30 10
158 101 322 E MAIN ST COLLECTOR 388 1 12 4 1,350 30 11
159 102 103 E MAIN ST COLLECTOR 258 1 12 4 1,350 30 11
160 103 104 TOLMAN CREEK ROAD COLLECTOR 564 1 12 4 1,350 30 11
City of Ashland H‐7 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
161 103 105 E MAIN ST COLLECTOR 834 1 12 4 1,350 30 11
162 104 103 TOLMAN CREEK ROAD COLLECTOR 564 1 12 4 1,350 30 11
163 104 320 TOLMAN CREEK ROAD COLLECTOR 627 1 12 4 1,350 30 11
164 105 103 E MAIN ST COLLECTOR 829 1 12 4 1,350 30 11
165 105 106 E MAIN ST COLLECTOR 1,621 1 12 4 1,350 30 11
166 106 107 E MAIN ST COLLECTOR 596 1 12 4 1,125 25 11
167 107 302 E MAIN ST COLLECTOR 2,061 1 12 4 1,125 25 11
168 108 70 OR‐66 MINOR ARTERIAL 974 2 12 4 1,750 30 10
169 108 109 WALKER AVE LOCAL ROADWAY 646 1 12 4 1,750 25 10
170 108 110 OR‐66 MINOR ARTERIAL 1,378 2 12 4 1,900 30 10
171 108 124 WALKER AVE COLLECTOR 1,842 1 12 2 900 20 10
172 109 108 WALKER AVE LOCAL ROADWAY 646 1 12 4 1,750 25 10
173 109 111 OR‐99 COLLECTOR 1,431 1 12 4 1,700 45 13
174 110 113 OR‐66 MINOR ARTERIAL 1,026 2 12 4 1,750 30 10
175 110 315 NORMAL AVE LOCAL ROADWAY 640 1 12 4 1,125 25 10
176 111 275 OR‐99 COLLECTOR 253 1 12 4 1,700 40 13
177 112 99 E MAIN ST COLLECTOR 903 1 12 4 1,350 30 10
178 112 124 WALKER AVE COLLECTOR 1,592 1 12 2 900 20 10
179 113 312 FAITH AVE LOCAL ROADWAY 941 1 12 4 1,125 25 11
180 113 323 OR‐66 MINOR ARTERIAL 797 2 12 4 1,900 35 11
181 114 311 FAITH AVE LOCAL ROADWAY 676 1 12 4 1,125 25 13
182 114 317 OR‐99 COLLECTOR 1,012 1 12 4 1,700 40 13
183 115 82 CROWSON RD LOCAL ROADWAY 758 1 12 4 1,700 40 14
184 115 336 CROWSON RD LOCAL ROADWAY 890 1 12 4 1,700 40 14
185 116 65 E MAIN ST COLLECTOR 276 2 12 4 1,750 30 3
186 117 64 E RAPP RD LOCAL ROADWAY 876 1 12 4 1,125 25 4
187 117 187 TALENT AVE LOCAL ROADWAY 1,915 1 12 4 1,575 35 3
188 118 59 W JACKSON RD LOCAL ROADWAY 191 1 12 4 1,750 25 4
189 119 51 MAPLE ST LOCAL ROADWAY 478 1 12 4 1,125 25 6
190 120 215 S LAUREL ST LOCAL ROADWAY 421 1 12 4 1,125 25 6
191 121 48 N LAUREL ST LOCAL ROADWAY 1,055 1 12 4 1,125 25 7
192 121 146 W HERSEY ST LOCAL ROADWAY 596 1 12 4 1,125 25 7
City of Ashland H‐8 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
193 121 161 N LAUREL ST LOCAL ROADWAY 681 1 12 4 1,125 25 7
194 122 47 HELMAN ST LOCAL ROADWAY 1,495 1 12 4 1,750 25 7
195 122 121 W HERSEY ST LOCAL ROADWAY 630 1 12 4 1,125 25 7
196 122 160 HELMAN ST LOCAL ROADWAY 708 1 12 4 1,125 25 7
197 123 70 OR‐99 COLLECTOR 273 2 12 4 1,750 45 10
198 123 125 WIGHTMAN ST COLLECTOR 1,693 1 12 0 1,125 25 10
199 123 339 OR‐99 COLLECTOR 1,612 2 12 4 1,900 30 10
200 124 108 WALKER AVE COLLECTOR 1,841 1 12 2 1,750 20 10
201 124 112 WALKER AVE COLLECTOR 1,591 1 12 2 900 20 10
202 125 123 WIGHTMAN ST COLLECTOR 1,693 1 12 0 1,750 25 10
203 125 124 IOWA ST COLLECTOR 1,262 1 12 4 1,125 25 10
204 125 249 WIGHTMAN ST COLLECTOR 1,090 1 12 0 1,125 25 10
205 127 41 S MOUNTAIN AVE COLLECTOR 451 1 12 4 1,750 25 10
206 127 97 S MOUNTAIN AVE COLLECTOR 1,298 1 12 4 1,750 25 10
207 127 125 IOWA ST COLLECTOR 1,723 1 12 4 1,125 25 10
208 129 215 HIGH ST LOCAL ROADWAY 1,256 1 12 4 1,125 25 9
209 129 251 GRANITE ST LOCAL ROADWAY 408 1 12 4 1,125 25 9
210 130 250 GRANITE ST LOCAL ROADWAY 616 1 12 4 1,125 25 9
211 131 130 GRANITE ST LOCAL ROADWAY 1,496 1 12 4 1,125 25 9
212 132 131 GRANITE ST LOCAL ROADWAY 1,084 1 12 4 1,125 25 9
213 133 137 HOLLY ST LOCAL ROADWAY 1,220 1 12 4 1,125 25 9
214 133 143 GERSHAM ST LOCAL ROADWAY 493 1 12 4 1,125 25 9
215 134 43 OR‐99 COLLECTOR 429 2 12 4 1,900 30 9
216 134 44 E MAIN ST LOCAL ROADWAY 263 1 12 4 1,750 30 9
217 134 148 N 3RD ST LOCAL ROADWAY 252 1 12 4 1,125 25 9
218 135 142 LIBERTY ST LOCAL ROADWAY 1,344 1 12 4 1,125 25 9
219 135 235 ASHLAND ST LOCAL ROADWAY 357 1 12 4 1,125 25 9
220 136 137 HARRISON ST LOCAL ROADWAY 654 1 12 4 1,125 25 9
221 137 138 HARRISON ST LOCAL ROADWAY 514 1 12 4 1,125 25 9
222 137 142 HOLLY ST LOCAL ROADWAY 671 1 12 4 1,125 25 9
223 138 139 HARRISON ST LOCAL ROADWAY 440 1 12 4 1,125 25 9
224 138 141 IOWA ST LOCAL ROADWAY 652 1 12 4 1,125 25 9
City of Ashland H‐9 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
225 139 237 HARRISON ST LOCAL ROADWAY 190 1 12 4 1,125 25 9
226 141 42 LIBERTY ST LOCAL ROADWAY 252 1 12 4 1,750 25 9
227 142 141 LIBERTY ST LOCAL ROADWAY 491 1 12 4 1,125 25 9
228 143 134 GERSHAM ST LOCAL ROADWAY 1,494 1 12 4 1,125 25 9
229 143 217 IOWA ST LOCAL ROADWAY 968 1 12 4 1,125 25 9
230 144 147 E HERSEY ST LOCAL ROADWAY 2,465 1 12 4 1,125 25 7
231 144 3114 N MOUNTAIN AVE COLLECTOR 803 1 12 4 1,125 25 8
232 145 122 W HERSEY ST LOCAL ROADWAY 982 1 12 4 1,125 25 7
233 145 163 OAK ST COLLECTOR 1,730 1 12 4 1,125 25 7
234 146 49 W HERSEY ST LOCAL ROADWAY 457 1 12 4 1,125 25 7
235 147 145 E HERSEY ST LOCAL ROADWAY 227 1 12 4 1,125 25 7
236 148 238 LITHIA WAY MINOR ARTERIAL 462 2 12 4 1,750 20 9
237 149 150 WILMER ST LOCAL ROADWAY 629 1 12 4 1,125 25 6
238 150 49 WILMER ST LOCAL ROADWAY 869 1 12 4 1,125 25 6
239 150 119 SCENIC DR LOCAL ROADWAY 1,088 1 12 4 1,125 25 6
240 151 159 SHERIDAN ST LOCAL ROADWAY 1,062 1 12 4 1,125 25 6
241 151 286 SHERIDAN ST LOCAL ROADWAY 92 1 11 0 1,125 25 6
242 152 153 MONTE VISTA DR LOCAL ROADWAY 212 1 11 0 1,125 25 6
243 153 154 MONTE VISTA DR LOCAL ROADWAY 349 1 11 0 1,125 25 6
244 154 155 SCHOFIELD ST LOCAL ROADWAY 307 1 12 4 1,125 25 6
245 155 54 SCHOFIELD ST LOCAL ROADWAY 275 1 12 4 1,125 25 6
246 159 53 OR‐99 COLLECTOR 392 1 12 4 1,700 40 6
247 160 161 ORANGE ST LOCAL ROADWAY 687 1 12 4 1,125 25 7
248 161 121 N LAUREL ST LOCAL ROADWAY 681 1 12 4 1,125 25 7
249 161 162 ORANGE ST LOCAL ROADWAY 891 1 12 4 1,125 25 7
250 162 261 GLENN ST LOCAL ROADWAY 124 1 12 4 900 20 7
251 163 145 OAK ST COLLECTOR 1,730 1 12 4 1,125 25 7
252 163 164 OAK ST COLLECTOR 1,561 1 12 4 1,125 25 7
253 164 163 OAK ST COLLECTOR 1,561 1 12 4 1,125 25 7
254 164 168 OAK ST COLLECTOR 763 1 12 4 1,700 40 7
255 165 160 HELMAN ST LOCAL ROADWAY 741 1 12 4 1,125 25 7
256 166 165 HELMAN ST LOCAL ROADWAY 653 1 12 4 1,125 25 7
City of Ashland H‐10 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
257 167 164 E NEVADA ST LOCAL ROADWAY 804 1 12 4 1,125 25 7
258 167 166 HELMAN ST LOCAL ROADWAY 922 1 12 4 1,125 25 7
259 168 169 OAK ST COLLECTOR 663 1 12 4 1,700 40 7
260 169 170 OAK ST COLLECTOR 493 1 12 4 1,700 40 7
261 170 171 EAGLE MILL RD COLLECTOR 710 1 12 4 1,700 45 5
262 171 172 EAGLE MILL RD COLLECTOR 478 1 12 4 1,700 45 5
263 172 173 EAGLE MILL RD COLLECTOR 552 1 12 4 1,700 45 5
264 173 174 EAGLE MILL RD COLLECTOR 423 1 12 4 1,700 45 5
265 174 175 EAGLE MILL RD COLLECTOR 3,587 1 12 4 1,700 45 5
266 175 176 EAGLE MILL RD COLLECTOR 564 1 12 4 1,700 45 5
267 176 177 EAGLE MILL RD COLLECTOR 855 1 12 4 1,700 45 5
268 177 178 EAGLE MILL RD COLLECTOR 313 1 12 4 1,700 45 5
269 178 179 EAGLE MILL RD COLLECTOR 1,648 1 12 4 1,700 45 5
270 179 59 S VALLEY VIEW RD COLLECTOR 736 1 12 4 1,750 35 4
271 179 72 S VALLEY VIEW RD COLLECTOR 1,486 1 12 4 1,575 35 4
272 180 181 TALENT AVE LOCAL ROADWAY 547 1 12 4 1,125 25 4
273 181 182 TALENT AVE LOCAL ROADWAY 1,633 1 12 4 1,125 25 4
274 182 183 TALENT AVE LOCAL ROADWAY 500 1 12 4 1,125 25 4
275 183 184 TALENT AVE LOCAL ROADWAY 1,509 1 12 4 1,125 25 4
276 184 185 TALENT AVE LOCAL ROADWAY 366 1 12 4 1,125 25 4
277 185 62 CREEL RD LOCAL ROADWAY 1,270 1 12 4 1,125 25 4
278 185 186 TALENT AVE LOCAL ROADWAY 2,439 1 12 4 1,125 25 4
279 186 63 ARNOS LOCAL ROADWAY 954 1 12 4 1,125 25 4
280 186 117 TALENT AVE LOCAL ROADWAY 1,669 1 12 4 1,125 25 4
281 187 188 TALENT AVE LOCAL ROADWAY 515 1 12 4 1,575 35 3
282 188 206 TALENT AVE LOCAL ROADWAY 193 1 12 4 1,125 25 3
283 189 190 TALENT AVE LOCAL ROADWAY 351 1 12 4 1,350 30 1
284 190 1 COLVER RD LOCAL ROADWAY 196 1 12 4 1,750 40 1
285 190 191 COLVER RD LOCAL ROADWAY 1,077 1 12 4 1,700 40 1
286 191 192 COLVER RD LOCAL ROADWAY 375 1 12 4 1,700 40 1
287 192 92 COLVER RD LOCAL ROADWAY 4,566 1 12 4 1,700 50 1
288 193 248 W VALLEY VIEW RD LOCAL ROADWAY 521 2 12 4 1,750 40 4
City of Ashland H‐11 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
289 194 195 MOUNTAIN VIEW DR LOCAL ROADWAY 443 1 12 4 1,125 25 4
290 195 193 MOUNTAIN VIEW DR LOCAL ROADWAY 493 1 12 4 1,125 25 4
291 196 197 SUNCREST RD LOCAL ROADWAY 1,565 1 12 2 1,350 30 4
292 197 198 SUNCREST RD LOCAL ROADWAY 1,147 1 12 2 1,350 30 2
293 198 199 SUNCREST RD LOCAL ROADWAY 493 1 12 2 1,350 30 2
294 199 200 SUNCREST RD LOCAL ROADWAY 908 1 12 2 1,350 30 2
295 200 201 SUNCREST RD LOCAL ROADWAY 480 1 12 2 1,350 30 2
296 201 202 SUNCREST RD LOCAL ROADWAY 1,239 1 12 4 1,350 30 2
297 202 203 SUNCREST RD LOCAL ROADWAY 1,942 1 12 4 1,350 30 2
298 203 204 PAYNE RD COLLECTOR 1,294 1 12 2 675 45 2
299 205 196 W VALLEY VIEW RD LOCAL ROADWAY 829 1 12 4 1,350 30 4
300 206 231 E MAIN ST COLLECTOR 334 1 12 4 1,350 30 3
301 206 300 TALENT AVE LOCAL ROADWAY 503 1 12 4 1,350 30 3
302 207 206 MAIN ST LOCAL ROADWAY 1,951 1 12 4 1,125 25 3
303 208 207 WAGNER CREEK RD LOCAL ROADWAY 227 1 12 4 1,125 25 3
304 208 246 FOSS RD LOCAL ROADWAY 662 1 12 4 1,350 30 3
305 209 92 WALDEN RD LOCAL ROADWAY 2,505 1 12 4 1,700 45 3
306 210 208 WAGNER CREEK RD LOCAL ROADWAY 1,445 1 12 4 1,350 30 3
307 210 211 W RAPP RD COLLECTOR 2,601 1 12 4 1,350 30 3
308 211 212 E RAPP RD LOCAL ROADWAY 196 1 12 4 900 20 3
309 212 213 E RAPP RD LOCAL ROADWAY 631 1 12 4 1,125 25 3
310 213 117 E RAPP RD LOCAL ROADWAY 665 1 12 4 1,125 25 3
311 214 267 PEACHY RD LOCAL ROADWAY 1,226 1 12 4 1,125 25 13
312 214 3109 WALKER AVE LOCAL ROADWAY 2,024 1 12 4 1,125 25 13
313 215 48 S LAUREL ST LOCAL ROADWAY 457 1 12 4 1,125 25 7
314 217 138 IOWA ST LOCAL ROADWAY 277 1 12 4 1,125 25 9
315 217 240 SHERMAN ST LOCAL ROADWAY 884 1 12 4 1,750 25 9
316 218 228 OR‐99 MINOR ARTERIAL 370 2 12 4 1,750 25 9
317 219 203 SUNCREST RD COLLECTOR 551 1 12 4 1,700 45 2
318 220 219 SUNCREST RD COLLECTOR 434 1 12 4 1,700 45 1
319 221 247 SUNCREST RD COLLECTOR 1,127 1 12 4 1,350 30 1
320 221 292 CLEARVIEW DR COLLECTOR 235 1 12 4 1,125 25 1
City of Ashland H‐12 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
321 222 221 SUNCREST RD COLLECTOR 791 1 12 4 1,125 25 1
322 223 226 N MOUNTAIN AVE COLLECTOR 926 1 12 4 1,125 25 8
323 223 260 N MOUNTAIN AVE COLLECTOR 556 1 12 4 1,700 40 8
324 224 225 N MOUNTAIN AVE COLLECTOR 757 1 12 4 1,125 25 7
325 225 144 N MOUNTAIN AVE COLLECTOR 900 1 12 4 1,125 25 7
326 226 223 N MOUNTAIN AVE COLLECTOR 926 1 12 4 1,125 25 8
327 226 224 N MOUNTAIN AVE COLLECTOR 1,179 1 12 4 1,125 25 7
328 227 46 LITHIA WAY MINOR ARTERIAL 232 2 12 4 1,900 20 9
329 227 229 PIONEER ST COLLECTOR 260 1 12 4 1,750 20 9
330 228 134 OR‐99 COLLECTOR 433 3 12 4 1,750 30 9
331 229 218 OR‐99 MINOR ARTERIAL 486 2 12 4 1,750 25 9
332 229 227 PIONEER ST COLLECTOR 260 1 12 4 1,750 20 9
333 230 73 N VALLEY VIEW RD COLLECTOR 1,138 1 12 4 1,575 35 4
334 231 232 E MAIN ST COLLECTOR 237 1 12 4 1,350 30 3
335 232 116 E MAIN ST COLLECTOR 247 1 12 4 675 15 3
336 233 41 OR‐99 COLLECTOR 812 2 12 4 1,750 30 9
337 233 42 OR‐99 COLLECTOR 480 2 12 4 1,750 30 9
338 234 41 S MOUNTAIN AVE LOCAL ROADWAY 1,338 1 12 4 1,750 25 10
339 235 233 BEACH ST LOCAL ROADWAY 1,795 1 12 4 1,750 25 9
340 235 234 ASHLAND ST LOCAL ROADWAY 727 1 12 4 1,125 25 9
341 236 79
DEAD INDIAN MEMORIAL
RD LOCAL ROADWAY 344 1 12 4 900 20 14
342 237 42 OR‐99 COLLECTOR 681 2 12 4 1,900 30 9
343 237 240 OR‐99 COLLECTOR 462 2 12 4 1,750 30 9
344 238 45 LITHIA WAY MINOR ARTERIAL 360 2 12 4 1,900 20 9
345 238 228 2ND ST COLLECTOR 373 1 12 0 1,750 30 9
346 239 238 N 2ND AVE LOCAL ROADWAY 509 1 12 4 1,750 25 9
347 239 256 B ST LOCAL ROADWAY 1,023 1 12 2 1,125 25 9
348 240 43 OR‐99 COLLECTOR 677 2 12 4 1,900 30 9
349 240 237 OR‐99 COLLECTOR 462 2 12 4 1,900 30 9
350 241 48 OR‐99 COLLECTOR 355 1 12 4 1,125 25 7
351 242 59 OR‐99 COLLECTOR 1,134 2 12 4 1,750 50 5
City of Ashland H‐13 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
352 243 242 JACKSON RD LOCAL ROADWAY 192 1 12 4 900 20 5
353 244 60 OR‐99 COLLECTOR 1,680 1 12 8 1,700 55 4
354 245 64 OR‐99 MINOR ARTERIAL 447 2 12 4 1,750 45 4
355 246 209 FOSS RD LOCAL ROADWAY 4,780 1 12 4 1,700 40 3
356 247 220 SUNCREST RD COLLECTOR 1,348 1 12 4 1,700 45 1
357 248 90 W VALLEY VIEW RD LOCAL ROADWAY 738 1 12 4 1,350 30 4
358 249 98 WIGHTMAN ST COLLECTOR 528 1 12 0 1,125 25 10
359 249 125 WIGHTMAN ST COLLECTOR 1,090 1 12 0 1,125 25 10
360 250 129 GRANITE ST LOCAL ROADWAY 1,160 1 12 4 1,125 25 9
361 251 253 OR‐99 MINOR ARTERIAL 137 2 12 4 1,900 25 9
362 252 255 WATER ST LOCAL ROADWAY 313 1 10 2 900 20 9
363 252 256 B ST LOCAL ROADWAY 351 1 12 2 1,125 25 9
364 253 229 OR‐99 MINOR ARTERIAL 654 2 12 4 1,750 25 9
365 254 47 LITHIA WAY LOCAL ROADWAY 486 2 12 4 1,900 20 9
366 254 255 LITHIA WAY LOCAL ROADWAY 139 1 12 2 900 15 9
367 255 253 WATER ST LOCAL ROADWAY 129 1 10 2 900 20 9
368 256 46 OAK ST LOCAL ROADWAY 344 1 12 4 1,125 25 9
369 256 252 B ST LOCAL ROADWAY 352 1 12 2 1,125 25 9
370 256 305 OAK ST LOCAL ROADWAY 628 1 12 4 1,125 25 7
371 257 260 N MOUNTAIN AVE COLLECTOR 1,136 1 12 4 1,700 40 7
372 257 3112 EAGLE MILL RD COLLECTOR 335 1 12 4 1,350 30 5
373 258 259 E NEVADA ST COLLECTOR 3,363 1 12 4 1,700 40 8
374 259 223 E NEVADA ST COLLECTOR 618 1 12 4 1,700 40 8
375 260 223 N MOUNTAIN AVE COLLECTOR 556 1 12 4 1,700 40 8
376 260 257 N MOUNTAIN AVE COLLECTOR 1,136 1 12 4 1,700 40 7
377 261 50 GLENN ST LOCAL ROADWAY 575 1 12 4 900 20 7
378 262 236
DEAD INDIAN MEMORIAL
RD LOCAL ROADWAY 2,402 1 12 4 1,700 50 12
379 263 303 TOLMAN CREEK ROAD COLLECTOR 1,830 1 12 4 1,750 30 13
380 264 114 OR‐99 COLLECTOR 551 1 12 4 1,700 40 13
381 265 264 PARK ST LOCAL ROADWAY 2,181 1 12 4 1,125 25 13
382 266 265 CRESTVIEW DR LOCAL ROADWAY 643 1 12 4 1,125 25 13
City of Ashland H‐14 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
383 266 267 HILLVIEW DR LOCAL ROADWAY 1,009 1 12 4 1,125 25 13
384 267 111 HILLVIEW DR LOCAL ROADWAY 1,613 1 12 4 1,125 25 13
385 268 133 GUTHRIE ST LOCAL ROADWAY 1,316 1 12 4 1,125 25 9
386 268 135 ASHLAND ST LOCAL ROADWAY 2,007 1 12 4 1,125 25 9
387 269 234 ASHLAND ST LOCAL ROADWAY 271 1 12 4 1,125 25 10
388 270 269 ELKADER ST LOCAL ROADWAY 1,314 1 12 4 1,125 25 10
389 271 270 ELKADER ST LOCAL ROADWAY 1,412 1 12 4 1,125 25 13
390 274 214 PINECREST LOCAL ROADWAY 926 1 12 4 900 20 13
391 275 264 OR‐99 COLLECTOR 600 1 12 4 1,700 40 13
392 275 315 NORMAL AVE LOCAL ROADWAY 952 1 12 4 1,125 25 13
393 276 96 8TH ST LOCAL ROADWAY 655 1 12 4 1,125 25 9
394 277 239 B ST LOCAL ROADWAY 352 1 12 4 1,125 25 9
395 277 298 B ST LOCAL ROADWAY 732 1 12 4 1,125 25 9
396 278 279 E NEVADA ST LOCAL ROADWAY 723 1 12 4 1,125 25 6
397 279 280 E NEVADA ST LOCAL ROADWAY 228 1 12 4 1,125 25 6
398 280 281 E NEVADA ST LOCAL ROADWAY 603 1 12 4 1,125 25 7
399 281 282 E NEVADA ST LOCAL ROADWAY 744 1 12 4 1,125 25 7
400 282 161 N LAUREL ST LOCAL ROADWAY 1,946 1 12 4 1,125 25 7
401 282 167 E NEVADA ST LOCAL ROADWAY 849 1 12 4 1,125 25 7
402 283 149 WILMER ST LOCAL ROADWAY 617 1 12 4 1,125 25 6
403 283 288 WALNUT ST LOCAL ROADWAY 1,324 1 12 4 1,125 25 6
404 284 283 WILMER ST LOCAL ROADWAY 737 1 12 4 1,125 25 6
405 285 284 WILMER ST LOCAL ROADWAY 629 1 12 4 1,125 25 6
406 286 151 SHERIDAN ST LOCAL ROADWAY 92 1 11 0 1,125 25 6
407 286 152 MONTE VISTA DR LOCAL ROADWAY 98 1 11 0 1,125 25 6
408 287 288 WILEY ST LOCAL ROADWAY 387 1 12 4 1,125 25 6
409 288 151 WALNUT ST LOCAL ROADWAY 797 1 12 4 1,125 25 6
410 289 150 SCENIC DR LOCAL ROADWAY 657 1 12 4 1,125 25 6
411 290 284 WRIGHTS CREEK DR LOCAL ROADWAY 638 1 12 4 1,125 25 6
413 292 221 CLEARVIEW DR COLLECTOR 235 1 12 4 1,125 25 1
414 292 293 CLEARVIEW DR COLLECTOR 231 1 12 4 1,125 25 1
415 293 292 CLEARVIEW DR COLLECTOR 231 1 12 4 1,125 25 1
City of Ashland H‐15 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
416 293 294 CLEARVIEW DR COLLECTOR 406 1 12 4 1,125 25 1
417 294 66 OR‐99 MINOR ARTERIAL 729 2 12 4 1,900 45 1
418 294 293 CLEARVIEW DR COLLECTOR 406 1 12 4 1,125 25 1
419 295 229 PIONEER RD LOCAL ROADWAY 346 1 12 0 1,750 30 9
420 296 228 2ND ST COLLECTOR 372 1 12 0 1,750 30 9
421 297 248 DRIVEWAY LOCAL ROADWAY 225 1 12 4 1,750 25 4
422 298 276 B ST LOCAL ROADWAY 1,123 1 12 4 1,125 25 9
423 298 277 B ST LOCAL ROADWAY 732 1 12 4 1,125 25 9
424 299 298 5TH ST LOCAL ROADWAY 467 1 12 4 1,125 25 9
425 300 189 TALENT AVE LOCAL ROADWAY 730 1 12 4 1,350 30 1
426 300 206 TALENT AVE LOCAL ROADWAY 503 1 12 4 1,350 30 3
427 300 294 NEW ST LOCAL ROADWAY 483 1 12 4 1,125 25 1
428 301 300 SUNNY ST LOCAL ROADWAY 441 1 12 4 1,125 25 1
429 302 77 E MAIN ST COLLECTOR 83 1 12 4 1,125 25 12
430 303 40 TOLMAN CREEK ROAD COLLECTOR 2,918 1 12 4 1,750 30 13
431 304 298 5TH ST LOCAL ROADWAY 455 1 12 4 1,125 25 9
432 305 145 OAK ST LOCAL ROADWAY 733 1 12 4 1,125 25 7
433 306 9 TOLMAN CREEK ROAD COLLECTOR 653 1 12 4 1,750 30 11
434 306 320 TOLMAN CREEK ROAD COLLECTOR 1,098 1 12 4 1,350 30 11
435 307 9 TOLMAN CREEK ROAD COLLECTOR 1,160 1 12 4 1,750 30 11
436 307 308 TOLMAN CREEK ROAD COLLECTOR 1,137 1 12 4 1,350 30 13
437 308 40 TOLMAN CREEK ROAD COLLECTOR 1,357 1 12 4 1,750 30 13
438 308 307 TOLMAN CREEK ROAD COLLECTOR 1,137 1 12 4 1,350 30 13
439 309 308 DIANNE ST LOCAL ROADWAY 656 1 12 0 1,125 25 13
440 310 307 TAKELMA WAY LOCAL ROADWAY 643 1 12 0 1,125 25 13
441 311 114 FAITH AVE LOCAL ROADWAY 676 1 12 4 1,125 25 13
442 311 312 FAITH AVE LOCAL ROADWAY 605 1 12 4 1,125 25 13
443 312 113 FAITH AVE LOCAL ROADWAY 941 1 12 4 1,750 25 11
444 312 311 FAITH AVE LOCAL ROADWAY 605 1 12 4 1,125 25 13
445 313 312 MAE ST LOCAL ROADWAY 311 1 12 1 1,125 25 13
446 314 311 WINE ST LOCAL ROADWAY 337 1 12 1 1,125 25 13
447 315 110 NORMAL AVE LOCAL ROADWAY 640 1 12 4 1,125 25 10
City of Ashland H‐16 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
448 315 275 NORMAL AVE LOCAL ROADWAY 952 1 12 4 1,125 25 13
449 316 315 FREMONT ST LOCAL ROADWAY 598 1 12 0 1,125 25 10
450 317 40 OR‐99 COLLECTOR 1,577 1 12 4 1,700 40 13
451 318 317 CLAY ST COLLECTOR 2,200 1 12 1 1,125 25 13
452 319 317 CLAY ST COLLECTOR 1,871 1 12 1 1,125 25 13
453 320 104 TOLMAN CREEK ROAD COLLECTOR 628 1 12 4 1,350 30 11
454 320 306 TOLMAN CREEK ROAD COLLECTOR 1,098 1 12 4 1,350 30 11
455 321 320 ABBOTT AVE LOCAL ROADWAY 654 1 12 0 1,125 25 11
456 322 102 E MAIN ST COLLECTOR 645 1 12 4 1,350 30 11
457 322 325 CLAY ST COLLECTOR 853 1 12 1 1,125 25 11
458 323 9 OR‐66 MINOR ARTERIAL 1,299 2 12 4 1,750 35 11
459 323 324 CLAY ST COLLECTOR 755 1 12 1 1,125 25 11
460 324 323 CLAY ST COLLECTOR 755 1 12 1 1,125 25 11
461 324 325 CLAY ST COLLECTOR 1,077 1 12 1 1,125 25 11
462 325 322 CLAY ST COLLECTOR 853 1 12 1 1,125 25 11
463 325 324 CLAY ST COLLECTOR 1,077 1 12 1 1,125 25 11
464 326 324 VILLARD ST LOCAL ROADWAY 423 1 12 1 1,125 25 11
465 327 325 CREEK DR LOCAL ROADWAY 849 1 12 1 1,125 25 11
466 328 123 INDIANA ST COLLECTOR 311 2 12 0 1,750 30 10
467 329 328 INDIANA ST COLLECTOR 927 1 12 0 1,350 30 10
468 329 3109 OREGON ST LOCAL ROADWAY 1,126 1 12 0 1,350 30 13
469 330 329 INDIANA ST COLLECTOR 947 1 12 0 1,350 30 13
470 331 74 SUTTON PL LOCAL ROADWAY 506 1 12 1 1,350 30 11
471 332 75 ASHLAND ST COLLECTOR 431 1 12 4 1,575 35 12
472 332 77 ASHLAND ST COLLECTOR 163 1 12 4 1,575 35 12
473 333 332 OAK KNOLL DR COLLECTOR 222 1 12 1 1,350 30 12
474 334 333 OAK KNOLL DR COLLECTOR 414 1 12 1 1,350 30 12
475 335 334 OAK KNOLL DR COLLECTOR 447 1 12 1 1,350 30 14
476 336 2 CROWSON RD LOCAL ROADWAY 2,355 1 12 4 1,750 40 14
477 336 115 CROWSON RD LOCAL ROADWAY 890 1 12 4 1,700 40 14
478 337 336 OAK KNOLL DR COLLECTOR 335 1 12 1 1,350 30 14
479 338 337 OAK KNOLL DR COLLECTOR 598 1 12 1 1,350 30 14
City of Ashland H‐17 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Link #
Up‐
Stream
Node
Down‐
Stream
Node Roadway Name Roadway Type
Length
(ft.)
No.
of
Lanes
Lane
Width
(ft.)
Shoulder
Width
(ft.)
Saturation
Flow
Rate
(pcphpl)
Free
Flow
Speed
(mph)
Grid
Number
480 339 41 OR‐99 COLLECTOR 540 2 12 4 1,750 30 10
481 339 123 OR‐99 COLLECTOR 1,611 2 12 4 1,750 30 10
482 340 269 ASHLAND ST LOCAL ROADWAY 173 1 12 4 1,125 25 10
483 340 339 UNIVERSITY WAY LOCAL ROADWAY 1,011 1 12 8 1,350 30 10
484 350 340 ASHLAND ST LOCAL ROADWAY 504 1 12 4 1,125 25 10
627 3109 109 WALKER AVE LOCAL ROADWAY 386 1 12 4 1,750 25 13
628 3110 226 FAIR OAKS AVE LOCAL ROADWAY 990 1 12 4 1,125 25 7
629 3111 170 EAGLE MILL RD COLLECTOR 952 1 12 4 1,700 40 5
630 3112 3111 EAGLE MILL RD COLLECTOR 1,507 1 12 4 1,700 40 5
631 3113 163 SLEEPY HOLLOW COLLECTOR 746 1 12 4 1,350 30 7
632 3114 144 N MOUNTAIN AVE COLLECTOR 803 1 12 4 1,125 25 8
633 3114 3115 N MOUNTAIN AVE COLLECTOR 416 1 12 4 1,125 25 10
634 3115 97 N MOUNTAIN AVE COLLECTOR 1,635 1 12 4 1,750 25 10
635 3115 3114 N MOUNTAIN AVE COLLECTOR 416 1 12 4 1,125 25 10
636 3116 3115 VILLAGE GREEN DR LOCAL ROADWAY 345 1 12 0 1,125 25 10
637 3117 98 WRIGHTMAN ST LOCAL ROADWAY 880 1 12 0 1,125 25 10
639 8002 31 I‐5 FREEWAY 2,852 2 12 8 2,250 70 1
(exit
link) 31 8002 I‐5 FREEWAY 2,852 2 12 8 2,250 70 1
(exit
link) 69 8003 OR‐99 MINOR ARTERIAL 757 1 12 4 1,275 45 1
(exit
link) 95 8005 COLVER RD LOCAL ROADWAY 2,638 1 12 4 900 50 1
(exit
link) 204 8006 PAYNE RD COLLECTOR 957 1 12 2 675 45 2
(exit
link) 13 8001 I‐5 FREEWAY 1446 2 12 4 2250 70 14
(exit
link) 88 8004 GREEN SPRINGS HWY COLLECTOR 1755 1 12 4 1575 35 14
City of Ashland H‐18 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Table H‐2. Nodes in the Link‐Node Analysis Network which are Controlled
Node
X
Coordinate1
(ft)
Y
Coordinate1
(ft)
Control Type Grid Map
Number
1 4301313 220754 Actuated 1
2 4334929 191395 TCP ‐ Actuated 13
7 4333870 196621 Actuated 11
8 4334445 196630 Actuated 11
9 4332741 196678 Actuated 11
40 4332645 193025 Actuated 13
41 4325289 198249 Actuated 10
42 4324223 198980 Stop 9
44 4322650 200221 Actuated 9
46 4321324 201453 Stop 9
47 4320711 201931 Actuated 9
48 4320275 202668 Actuated 7
49 4319856 203690 Stop 7
50 4319685 204310 Stop 6
51 4319451 204738 Actuated 6
54 4317710 206641 Stop 6
59 4314371 210093 Actuated 4
62 4307467 215682 Stop 4
63 4305647 217114 Stop 4
64 4304473 218065 Actuated 4
70 4327269 196853 Actuated 10
72 4314474 212309 Stop 4
73 4314326 213147 Stop 4
74 4335247 196844 Stop 11
77 4336285 196511 Stop 12
82 4338254 193425 Stop 14
90 4305235 219402 Stop 4
91 4306405 218950 Stop 4
92 4295153 220541 Stop 1
96 4324317 200079 Stop 9
97 4325348 199997 Actuated 10
98 4327091 200301 Stop 10
103 4332375 199426 Stop 11
108 4328242 196819 Actuated 10
109 4328228 196173 Actuated 10
110 4329620 196777 Stop 10
111 4329385 195330 Stop 13
112 4328320 200251 Stop 10
113 4330646 196733 TCP ‐ Actuated 11
114 4330526 194514 Stop 13
117 4303793 217512 Stop 3
City of Ashland H‐19 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Node
X
Coordinate1
(ft)
Y
Coordinate1
(ft)
Control Type Grid Map
Number
121 4320822 203570 Stop 7
122 4321376 203270 Stop 7
123 4327034 196991 Actuated 10
124 4328292 198660 Stop 10
125 4327030 198684 Stop 10
127 4325307 198700 Stop 10
134 4322397 200292 Actuated 9
135 4324166 196894 Stop 9
137 4323514 198258 Stop 9
138 4323551 198771 Stop 9
141 4324202 198729 Stop 9
142 4324185 198238 Stop 9
143 4322308 198801 Stop 9
144 4324941 202706 Stop 7
145 4322258 202839 Stop 7
148 4322498 200523 Stop 9
150 4318988 203662 Stop 6
151 4317750 205815 Stop 6
154 4317409 206362 Stop 6
159 4318812 205813 Stop 6
161 4321105 204189 Stop 7
163 4322693 204513 Stop 7
164 4322781 206072 Stop 7
170 4322507 207881 Stop 5
179 4314468 210823 Stop 4
185 4306733 214646 Stop 4
186 4305027 216389 Stop 4
190 4301153 220641 Stop 1
193 4303977 219436 Stop 4
203 4304283 223182 Stop 2
206 4302002 219362 Stop 3
214 4328167 193764 Stop 13
215 4319896 202412 Stop 7
221 4302431 220629 Stop 1
223 4325433 206361 Stop 8
226 4325010 205537 Stop 7
227 4321511 201317 Actuated 9
228 4322062 200567 Actuated 9
229 4321384 201090 Actuated 9
233 4324616 198704 Actuated 9
234 4325249 196912 Stop 10
235 4324522 196912 Stop 9
237 4323664 199368 Stop 9
City of Ashland H‐20 KLD Engineering, P.C.
Evacuation Time Estimate Study Rev. 0
Node
X
Coordinate1
(ft)
Y
Coordinate1
(ft)
Control Type Grid Map
Number
238 4322233 200898 Actuated 9
239 4322383 201385 Stop 9
240 4323291 199640 Actuated 9
242 4315261 209390 Actuated 5
248 4304498 219429 Actuated 4
249 4327086 199773 Stop 10
250 4320146 200298 Stop 9
252 4321133 201952 Stop 7
253 4320931 201558 Stop 9
255 4320995 201671 Yield 9
256 4321438 201777 Stop 9
261 4320194 204576 Stop 7
264 4330067 194818 Stop 13
267 4329392 193717 Stop 13
275 4329592 195185 Stop 13
284 4317006 203715 Stop 6
288 4317739 205018 Stop 6
294 4302273 219862 Stop 1
298 4323410 201040 Stop 9
300 4301792 219819 Stop 1
307 4332710 195518 Stop 13
308 4332696 194381 Stop 13
311 4330566 195189 Stop 13
312 4330585 195794 Stop 13
315 4329601 196137 Stop 10
317 4331347 193922 Stop 13
320 4332790 198429 Stop 11
322 4331493 199401 Stop 11
323 4331443 196716 Stop 11
324 4331448 197471 Stop 11
325 4331480 198548 Stop 11
332 4336150 196604 Stop 12
336 4336786 192844 Stop 14
339 4325709 197909 Stop 10
3109 4328229 195787 Stop 13
3115 4325406 201631 Stop 10
1Coordinates are in the North American Datum of 1983 Oregon South State Plane
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Figure H‐1. Evacuation Time Estimate Study Link‐Node Analysis Network
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Figure H‐2. Link‐Node Analysis Network – Grid 1
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Figure H‐3. Link‐Node Analysis Network – Grid 2
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Figure H‐4. Link‐Node Analysis Network – Grid 3
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Figure H‐5. Link‐Node Analysis Network – Grid 4
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Figure H‐6. Link‐Node Analysis Network – Grid 5
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Figure H‐7. Link‐Node Analysis Network – Grid 6
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Figure H‐8. Link‐Node Analysis Network – Grid 7
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Figure H‐9. Link‐Node Analysis Network – Grid 8
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Figure H‐10. Link‐Node Analysis Network – Grid 9
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Figure H‐11. Link‐Node Analysis Network – Grid 10
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Figure H‐12. Link‐Node Analysis Network – Grid 11
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Figure H‐13. Link‐Node Analysis Network – Grid 12
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Figure H‐14. Link‐Node Analysis Network – Grid 13
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Figure H‐15. Link‐Node Analysis Network – Grid 14
APPENDIX J
Evacuation Sensitivity Studies
“What‐if” Scenarios
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J. EVACUATION SENSITIVITY STUDIES
This appendix presents the results of a series of sensitivity analyses, or “what‐if” analyses. These
analyses are designed to identify the sensitivity of the Evacuation Time Estimate (ETE) to changes
in some base evacuation conditions.
J.1 Effect of Changes in Trip Generation Times
A sensitivity study was performed to determine whether changes in the estimated trip generation
(mobilization) time influence the ETE for an evacuation of all EMZs (Region R18). Specifically, if
the tail of the mobilization distribution were truncated (i.e., if those who responded most slowly
to the evacuation order, could be persuaded to respond much more rapidly) or if the tail were
elongated (i.e., spreading out the departure of evacuees to limit the demand during peak times),
how would the ETE be affected? These “what if” scenarios were considered for a fall, midweek,
midday scenario (Scenario 4). Results are tabulated in Table J‐1. Trip Generation times of 3 hours,
4 hours (Base) and 5 hours were tested.
As seen shown in Table J‐1, if evacuees mobilize in one less hour the 90th and 100th percentile
ETEs is reduced by 5 minutes and 20 minutes, respectively. If evacuees were to mobilize in one
hour more, the 90th percentile ETE remains the same while the 100th percentile ETE increases by
one hour. As discussed in Section 7, congestion within the ETE exists within the EMZs for just
over 3 hours and 30 minutes. As such, if the time to mobilize is less than 3 hours and 40 minutes,
congestion dictates the 100th percentile ETE. If the time to mobilize is longer than 3 hours and
40 minutes, the 100th percentile is dictated by the trip generation time.
J.2 Effect of Changes in the Number of People in the Shadow Region Who Relocate
A sensitivity study was conducted to determine the effect on ETE due to changes in the
percentage of people who decide to relocate from the Shadow, see Figure 7‐1. The case
considered was Scenario 4, Region R18; a fall, midweek, midday evacuation of the entire EMZ.
The movement of people in the Shadow Region has the potential to impede vehicles evacuating
from an Evacuation Region. Refer to Sections 3.2 and 7.1 for additional information on
population within the Shadow Region. Shadow evacuation percentages of 0 and 100 were tested
to bound the analysis.
Table J‐2 presents the ETE for each of the cases considered. The results show that decreasing the
shadow population to 0 percent reduces the 90th percentile ETE by 5 minutes while the 100th
percentile ETE remains the same. A full evacuation (100%) of the Shadow Region increases the ETE
by 10 minutes for the 90th percentile and 5 minutes for the 100th percentile – not a significant
change.
The Shadow Region was defined as the area beyond the EMZ including the City of Talent to the
north, Emigrant Lake to the south and the surrounding ridgelines. All of these areas are sparsely
populated areas. Therefore, changes in the percentage of people that decide to voluntarily evacuate
beyond the city limits will have little to no impact on an evacuation of the City of Ashland.
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J.3 Effect of Reducing the Evacuation Demand – One Vehicle per Household
The relationship between supply and demand is very important in computing evacuation time.
Evacuation travel supply is the ability of the roadway network to serve the traffic demand
(number of evacuating vehicles) during an emergency. In this context, when the demand exceeds
the supply (available capacity), congestion occurs causing delay and prolonging the evacuation.
The roadway capacity is often difficult to increase as its expensive and difficult to widen existing
infrastructure or build additional roadways. Thus, it is good practice to attempt reduce the
evacuating traffic demand such that demand does not exceed capacity. The demographic survey
of the EMZs indicated residents would use approximately 1.43 vehicles per household (HH)
during an evacuation (see Appendix F). A sensitivity study was conducted to determine the effect
on ETE when the evacuating vehicles per household was reduced to one (a 43% reduction in
evacuating vehicles).
As seen in Table J‐3, during the base case scenario, there are 13,666 residential vehicles
evacuating from the EMZ (see Table 3‐4). When the number of evacuation vehicles per household
is reduced to one, the number of evacuating residential vehicles is reduced to 9,560. The case
considered was Scenario 3, Region R18; a fall, midweek, midday scenario and an evacuation of
all EMZs. When the evacuating traffic demand is reduced by approximately 40%, the 90th
percentile ETE is reduced by 30 minutes – a significant change – and the 100th percentile ETE is
not affected. The 100th percentile ETE is still dictated by congestion. Efforts should be made to
inform the general population that reducing the number of vehicles each household uses during
an evacuation can greatly reduce the amount of time needed to evacuate the area.
J.4 Effect of Direction of Wildfire Approach
Depending on the origin and prevailing winds, a wildfire can block one or more egress routes out
of the EMZ. Alternatively, emergency officials could decide to reserve egress routes for first
responders and emergency vehicles and only use I‐5 northbound or southbound for evacuees.
Two cases were run to simulate various roadway cases:
1. A scenario wherein a wildfire is to the south of the city and all traffic is forced northbound,
and
2. A scenario wherein a wildfire is to the north of the city and all traffic is forced southbound.
These two cases were run for Scenario 4, Region R18; a fall, midweek, midday scenario for an
evacuation of all EMZs. The results are shown in Table J‐4.
J.4.1 A Wildfire to the North wherein Traffic is Forced Southbound
This case was run to represent a wildfire event that originates to the north‐west of the Ashland
City limits and evacuation to the north along I‐5 and OR‐99 are not feasible. As shown in Table
J‐4, when EMZ evacuees are unable to evacuate northbound due to the proximity of the
approaching wildfire, the 90th percentile ETE and 100th percentile ETE increase by 3 hours and 10
minutes and 3 hours and 25 minutes, respectively.
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Under these circumstances, vehicles are forced to reroute to I‐5 southbound, OR‐99 southbound
(which eventually merges with I‐5), and OR‐66 southbound. These roadways, or ramps giving
access to these roadways, are oversaturated. As a result, congestion within the EMZ worsens,
delays increase, and ETE increases when compared to the base case.
J.4.2 A Wildfire to the South wherein Traffic is Forced Northbound
This case was run to represent a wildfire event that originates south of the City limits and
evacuation to the south along I‐5, OR‐99 and OR‐66 are not feasible. Table J‐4 shows the 90th
and 100th percentile ETE results when EMZ evacuees are unable to evacuate southbound due to
the proximity of the approaching wildfire. The 90th percentile ETE increases by 2 hours and 25
minutes and the 100th percentile ETE increases by 2 hours and 35 minutes for this scenario.
Under these circumstances, vehicles are forced to reroute to OR‐99 northbound and I‐5
northbound. OR‐99 is oversaturated and the ramps that give access to I‐5 are oversaturated. As
a result, congestion within the EMZ worsens, delays increase, and ETE increases when compared
to the base case.
J.4.3 Patterns of Traffic Congestion due to Wildfire Approach
Figure J‐1 through Figure J‐8 show the patterns of congestion for the wildfire cases discussed
above and the base case at each hour into the evacuation up to 7 hours and 25 minutes after the
advisory to evacuate. Case 1 is the case wherein there is a wildfire to the north and traffic is
forced to the south. Case 2 is the case wherein there is a wildfire to the south and traffic is forced
to the north. Case 3 is the base case wherein traffic can choose to go north or south.
At 1 hour into the evacuation, as shown in Figure J‐1, all cases show peak congestion in the EMZs.
Congestion along OR‐99, OR‐66, and E Main St is severe in all cases. When compared to Case 3,
Downtown Ashland (EMZs 2, 8, and 10) are less congested because those vehicles are now forced
south and cause the south of the EMZ (EMZs 3, 7, 4, and 6) to be more congested for Case 1.
When comparing the Case 3 to Case 2, congestion patterns within the EMZ are similar, except for
worse congestion in EMZ 8 and 9 as vehicles reroute along N Mountain Rd to gain access to Oak
St to evacuate the area northbound. Congestion along Crowson Rd and OR‐99 southbound and
OR‐66 southbound is less in the southern wildfire case as vehicles are not permitted to evacuate
in that direction.
Figure J‐2 compares the patterns of congestion at 2 hours into the evacuation for all wildfire
cases. When comparing Case 1 to Case 2, congestion in EMZs 2, 8, 9, and 10 is less, but congestion
in EMZs 3, 4, 5, 6, and 7 is worse. The vehicles from the northern EMZs are getting stuck in the
central and southern EMZs as OR‐99, OR‐66, and the I‐5 on ramps process all of the vehicles that
are attempting to use them to evacuate.
Alternatively, comparing Case 2 to Case 3, congestion patterns are similar, but congestion in
EMZs 8 and 9 are worse due to traffic along N Mountain Rd, and congestion in EMZ 5 is better
since vehicles cannot evacuate in that direction.
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Figure J‐3 compares congestion patterns at 3 hours after the advisory to evacuate. In this figure,
the impacts of the loss of the northbound evacuation routes in Case 1 and the southbound
evacuation routes in Case 2 become obvious. In Cases 1 and 2, OR‐99 remains more congested ‐
the direction of congestion is dependent on the wildfire case – than in Case 3. EMZ 3 is more
congested in Cases 1 and 2 than Case 3. When comparing Case 1 to Case 3, EMZs 1, 2, 9, and 10
are mostly clear of congestion. All other EMZs, however are more congested in Case 1 than Case
3. Looking at Case 2, congestion within the EMZ is mostly equally dispersed as vehicles attempt
to access the only I‐5 ramp within the EMZ (along Ashland St/OR‐66) and OR‐99 northbound.
Figure J‐4 shows the pattens of congestion at 4 hours after the advisory to evacuate for all wildfire
cases. At this time, Case 3 is completely clear of congestion. Cases 1 and 2 continue to show
severe congestion within the EMZ. OR‐99 and OR‐66/Ashland St to access the I‐5 on ramp remain
at LOS F. Congestion for Case 1 is consolidated to central and southern Ashland, while congestion
for Case 2 is dispersed across nearly all EMZs.
When comparing the patterns of congestion at 5 hours into the evacuation for all closure cases,
shown in Figure J‐5, congestion only occurs on OR‐99 and local roadways that intersect OR‐99 for
Case 1. For Case 2, severe congestion remains on the majority of OR‐99 northbound and OR‐66
east and westbound. Oak St continues to exhibit congested conditions as well, as vehicles
attempt to find alternative routes to evacuate the area.
At 6 hours into the evacuation, as shown in Figure J‐6, congestion has dissipated in both Case 1
and Case 2, but still remains along OR‐99 in Case 1 and in EMZ 6 in Case 2.
Figure J‐7 shows the patterns of congestion at 7 hours after the advisory to evacuate. All
congestion in Case 2 has cleared at this time as all evacuees cleared the area at 6 hours and 35
minutes after the advisory to evacuate (see Table J‐4). Congestion remains along OR‐99
southbound in Case 1.
Figure J‐8 shows the last remnants of congestion in Case 1 along OR‐99 southbound and Crowson
Rd at 7 hours and 20 minutes after the advisory to evacuate. All congestion clears (and all
vehicles successfully evacuate the area) 5 minutes later for this case.
J.5 Additional “What‐if” Scenarios
Local officials from the City of Ashland requested three additional “what‐if” scenarios looking
into the potential impacts to evacuation of the addition of the bridge over Bear Creek connecting
E Nevada St, additional access to I‐5 via Freeway ramps on N Mountain Ave and if a combination
of both roadway improvements. Figure J‐9 shows the location of the bridge and on ramps.
Table J‐5 represents the 90th and 100th percentile ETEs for all three cases. The addition of the E
Nevada St bridge over Bear Creak decreases the 90th percentile ETE by 10 minutes. Adding on
ramps to I‐5 near N Mountain Ave also decreases the 90th percentile ETE by 10 minutes. Adding
both the bridge and the I‐5 on ramps reduced the 90th percentile ETE by 15 minutes. The 100th
percentile ETE remained unchanged for all cases since it is dictated by the time needed to
mobilize, and in all cases, the trip generation time is 4 hours.
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Table J‐1. Evacuation Time Estimates for Trip Generation Sensitivity Study
Trip Generation Time
Evacuation Time Estimates for All EMZs
90th Percentile 100th Percentile
3 Hours 3:05 3:40
4 Hours (Base) 3:10 4:00
5 Hours 3:10 5:00
Table J‐2. Evacuation Time Estimates for Shadow Sensitivity Study
Percent Shadow Evacuation Evacuating Shadow3
Evacuation Time Estimate for All EMZs
90th Percentile 100th Percentile
0 0 3:05 4:00
6 (Base) 451 3:10 4:00
100 6,490 3:20 4:10
3 The Evacuating Shadow Vehicles, in Table J-2, represent the residents who will spontaneously decide to relocate during the evacuation. The basis, for the base values shown, is a 6%
relocation of shadow residents. See Section 6 for further discussion.
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Table J‐3. Evacuation Time Estimates for Reduction in Demand
Case Evacuating Resident
Vehicles
Evacuation Time Estimates for All EMZs
90th Percentile 100th Percentile
One Vehicle per HH 9,560 2:40 4:00
Base Case
(1.43 Evacuating Vehicles per HH) 13,666 3:10 4:00
Table J‐4. Evacuation Time Estimates for Direction of Wildfire Approach
Case
Evacuation Time Estimates for All EMZs
90th Percentile ETE 100th Percentile
Base Case 3:10 4:00
Wildfire to the North
(Northbound Roadways Closed –
Traffic forced Southbound)
6:20 7:25
Wildfire to the South
(Southbound Roadways Closed –
Traffic forced Northbound)
5:35 6:35
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Figure J‐1. Wildfire Approach Congestion Pattern Comparison at 1 Hour after the Advisory to Evacuate
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Figure J‐2. Wildfire Approach Congestion Pattern Comparison at 2 Hours after the Advisory to Evacuate
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Figure J‐3. Wildfire Approach Congestion Pattern Comparison at 3 Hours after the Advisory to Evacuate
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Figure J‐4. Wildfire Approach Congestion Pattern Comparison at 4 Hours after the Advisory to Evacuate
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Figure J‐5. Wildfire Approach Congestion Pattern Comparison at 5 Hours after the Advisory to Evacuate
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Figure J‐6. Wildfire Approach Congestion Pattern Comparison at 6 Hours after the Advisory to Evacuate
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Figure J‐7. Wildfire Approach Congestion Pattern Comparison at 7 Hours after the Advisory to Evacuate
City of Ashland J‐14 KLD Engineering, P.C.
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Figure J‐8. Wildfire Approach Congestion Pattern Comparison at 7 Hours and 20 Minutes after the Advisory to Evacuate
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Figure J‐9. E Nevada St Bridge and I‐5 On Ramps
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Table J‐5. Evacuation Time Estimates for Additional “What‐if” Scenarios
Case
Evacuation Time Estimates for All EMZs
90th Percentile ETE 100th Percentile
Base Case 3:10 4:00
E Nevada St over Bear Creek 3:00 4:00
I‐5 Ramps near Nevada St 3:00 4:00
E Nevada St over Bear Creek and I‐5 Ramps 2:55 4:00
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Transportation Commission
Action Item List
A p r i l 1 5 , 2 0 2 1
Action Items:
1. Capital Improvement Plan-Review and Recommendation (2020/21)
• Review proposed roadway, pedestrian and bicycle network CIP projects for the 2021-2023
budget biennium
• Make recommendation on priorities for 2 and 6-year CIP projects
2. TSP Update (2020-21)
• Solicitation documents have been submitted and scored by project team
• Scope, schedule and fee documents under review (TC December 2019/January 2020/February
2020)
• Professional services contract requires Council approval
• Schedule Council approval (April 7, 2020)
• TSP Postponed until timing to start project is more appropriate (FY22/23)
3. Main St. Crosswalk truck parking (no change)
• Analysis is included in the revitalize downtown Ashland plan and was recently discussed during
the kickoff meeting.
• The Revitalize Downtown Ashland Transportation Growth and Management grant project has
begun that will assess safety and parking in the downtown core. (February 2020) No change-
March 2020
• The Revitalize Downtown Ashland Project has been cancelled with the expectation to re-start
the project at a more appropriate time in the future (1-2 years).
4. Siskiyou Blvd. and Tolman Creek Intersection Improvements
• The Oregon Department of Transportation removed median island and restriped Tolman
Creek portion of intersection to allow for better right-hand turning truck movements.
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• The Oregon Department of Transportation is also looking at curb ramp design changes to the
intersection. (February 2020) No change-March 2020
• Reference ODOT Intersection Change Schematic Drawing (September 2020)
• Forwarded TC comments to ODOT regarding review of 60% Design (September 2020)
• ODOT Provided Advance Plans of intersection redesign (March 2021)
5. 20 is Plenty Subcommittee Work (November 2021 start)
• Mark Brouillard is participating in the 20 mph is plenty subcommittee work with the Climate
Policy Commission representatives.
• Commission endorsed recommendation developed in the 20 is Plenty report discussed at the
January 2021 meeting. Next steps include continued discussion of program and associated
strategies for public outreach (education, engineering, enforcement, evaluation), inclusion
into the TSP update, updating CIP, and holding a formal Council discussion.
• 20 Is Plenty programmatic discussion to be scheduled for April 2021.
6. Railroad District Parking Limitations Review
• At a future meeting TBD, discuss current parking limitations in railroad district.
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Memo
Date:
April 8, 2021
From: Scott A. Fleury
To: Transportation Commission
RE: Normal Avenue Traffic Calming Phase 1
BACKGROUND:
Public Works previously received a request/signature petition for the traffic calming program for
the section of Normal Avenue between Siskiyou Boulevard and Ashland Street.
Normal Avenue is classified as an Avenue Transportation System Plan (TSP) (1998, 2013). The
City of Ashland has adopted Street Design standards that define roadway cross sections and
anticipated volumes, reference figure 1 below.
Currently Normal Avenue has a curb to curb width of 31 feet. Parking is allowed on both side of
the road. There is sidewalk on the east side for the full length of the section. A bike lane
improvement is defined in the current TSP:
Figure 1: Avenue Cross Sections
Staff has included the complete speed and volume data collected for the Normal Avenue study
and it is attached for reference. The 85% speed (maximum) was 28.1mph with a maximum
average daily traffic of 725 vehicles.
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The two accidents along Normal Avenue since 2015 were fixed object crashes, not correctable
by traffic calming.
Based on the current scoring developing within the program Normal Avenue would generate
three (3) total points, with eight (8) required for any phase 2 actions.
CONCLUSION:
This is item is informational at this time as their will be future discussions about potential
changes to the traffic calming program. Staff will work with the Police Department to have the
radar trailer placed onsite and perform targeted enforcement as specified for phase 1 of the
program.