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Developing Evacuation Model using Dynamic
Traffic Assignment
ChiPing Lam, Houston-Galveston Area CouncilDr. Jim Benson, Texas Transportation InstitutePeter Mazurek, Citilabs
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Motivation
In September 2005, Hurricane Rita landed east of Houston
Well over 1 million people attempted to evacuate from the eight county region
Severe congestion as a results
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Retreat!
Evacuation routes became “parking lots”.
Some people spent more than 18 hours on the evacuation routes
Fatal accidents, abandoned cars, and other safety issues
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Crawling Speed US 290 WB FM 1960 to Barker Cypress
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Time from 9/21 midnight
Sp
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In response…
H-GAC coordinated with various governmental agencies to develop a hurricane evacuation plan.
H-GAC was asked to develop a tool for evacuation planning.
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Goal of this model
Re-generate the Rita evacuations Provide evacuation demands Estimate traffic volumes and delays Sensitive to various scenarios and plans Apply to non-evacuation planning
(corridor, sub-area, ITS, etc)
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Challenge - Model Size
8-county region with 4.7 million population in 2000 and is expected to grow to over 7.7 million by 2035.
3000 zones and 43,000 links 7,700 Square miles from CBD to rural area Around 14,000,000 daily trips modeled Long trip: average work trip length over 20
minutes, with almost 10% over 40 minutes
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Challenge - Demands
Little Survey data for Rita event Future evacuation demands could be
varied in– Time period– Response rate and number of trips– Origin and Destination
Interaction between evacuation, normal daily, and non-evacuation traffic
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Challenge - Network
Network change during evacuation Sensitive to Policy Factors
– Contra-flow lane– Shoulder lane use– HOV lane opens to public– Ramp closure– Signal timing
Facilities become unavailable due to flooding, high wind, or other disasters
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H-GAC Expectation
Validation – Normal Day Traffic– Rita– Year 2010 Scenario
Able to adjust evacuation trip tables for different situations
Sensitive to policy factors Allow road changes within evacuation
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Estimation Of Hurricane Evacuation Demand Models
Jim Benson
Texas Transportation Institute
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Today’s Presentation
Study Area And Data Base Trip Generation Models Trip Distribution Models Time-of-day Factors
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H-GAC Study Area
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Houston TranStar Rita Evacuation Survey
Solicited participation on website Participants responded to questions online 6,570 respondents 6,286 usable household responses 3,886 households evacuated by car or
truck
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Evacuation Generation Models
Models developed for Rita event
Structured to facilitate exploring different evacuation scenarios
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APPROACH
Six-day event modeled Cross-classification variables:
– 6 geographical districts– 5 household size groups
Production models:– Probability of evacuating– Vehicle trips/evacuation household– Trip purpose split
Simple attraction models Non-resident trip models
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Six Districts
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Dist 1 Dist 2 Dist 3 Dist 4 Dist 5 Dist 6
Percent of Households Evacuating
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Dist 1 Dist 2 Dist 3 Dist 4 Dist 5 Dist 6
1 pph
3 pph
5+ pph
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
Vehichle Trips/Evacuated Household
1 pph
2 pph
3 pph
4 pph
5+ pph
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Dist 1 Dist 2 Dist 3 Dist 4 Dist 5 Dist 6
Percent of Internal Trips Exiting
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Internal Evacuation Attractions
Households 83.3%
Hotels & Motels 8.4%
Public Shelter 2.0%
Other 6.3%
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External Station Evacuation Attractions
Distributed attractions to other urban areas based on their population and relative accessibility
Allocated results to external stations
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Rita Evacuation Generation Results
Internal-Internal 218,785
Internal-External 1,040,936
External-Internal
(non-residents)
5,406
External-External
(non-residents)
21,617
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Two Trip Distribution Models
Evacuation trips to internal zones
Evacuation trips to external stations
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Distribution Model For Internal Attractions
Essentially a “constrained interactance” model
No friction factors No iterative process Constrained to productions Interaction constraint Productions allocated to eligible attraction
zones based on relative attractiveness
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Interaction Constraint
No attractions to zones in the 3 mandatory evacuation areas
Eligible attraction zones must be either:– Further from the coast, OR– 80+ miles from the coast
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Zones By Distance From Coast
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Distribution Model For External Station Attractions
Similar to traditional external-local models using a gravity model
Primary difference is that the external stations are treated the attractions
Somewhat relaxed version of the normal external-local friction factors used
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TRIP DISTRIBUTION RESULTS(normal off-peak speed travel time minutes)
Trip Purpose
Trips
Average
Trip
Length
90th %-tile
Trip
Length
Max
Trip
Length
Trips to
Internal
Zones
224,189 47 78 156
Trips to
External
Stations
1,062,757 72 104 176
All
Evacuation Trips
1,286,946 68 176
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Time-of-day Factors
Estimated from survey data
Developed for each of the six districts
Hourly Distribution for 6-day Event
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% Distribution of Trips by Day
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0 1 2 3 4 5 6 7
Day of 6-day Event
%
Dist 1
Dist 2
Dist 3
Dist 4
Dist 5
Dist 6
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Percent of Evacuation Trips by Hour
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0 10 20 30 40 50 60 70 80
Hour of 72-hour period (days 3-5)
% o
f E
ven
t T
rip
s Dist 1
Dist 2
Dist 3
Dist 4
Dist 5
Dist 6
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Developing Alternative Scenarios
Consider adjustments to % households evacuating by district
Consider adjusting hourly distributions by district
Consider adjusting vehicle trip rates to reflect taking fewer vehicles by district
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Next Step is Assignment….
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Evacuation Model Development
using Cube Avenue
Pete MazurekDirector of Consulting ServicesCitilabs, Inc.
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Input Parameter Types Tool for Evacuation/Event Planning
Needs to be sensitive to variations in THREE distinct types of inputs:
– Situational (Event-Specific) parameters
– Policy Change Inputs
– System and Background Inputs
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Two Stages of The Evacuation Model Tool
Travel System Response and Performance
Process #1: Demand Profile Generator
System Demand Profile
Process #2: System Analyzer
Situation Inputs Policy Inputs System Inputs
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System Demand Profile
Background Demand– Everyday, regular “average weekday” trips – Stratified by hour for a 24-hour period– 3 successive weekday periods to comprise 72-hours
prior to storm landfall– Progressively attenuated because regular trips are
not taken once people evacuate
Evacuation Demand– The primary trip out of the storm’s path– Stratified by hour for 72 hours prior to landfall
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Dynamic Traffic Assignment (DTA)
Method of system-level (regional) assignment analysis which seeks to track the progress of a trip through the regional network over time
Accounts for buildup of queues due to congestion and/or incidents
A bridge between traditional region-level static assignment and corridor-level micro-simulation
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Why use DTA?
Why NOT use traditional (Static) assignment?– No impact of queues– No ability to deal with upstream impacts– Links do not directly affect each other– Not conducive to time-series analysis
Why NOT use traffic micro-simulation?– Study area of interest too large and complex– Too much data and memory required– Too many uncertainties to model accurately
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Cube Avenue (DTA Module)
Add-on module to provide DTA capability for the Cube/ Cube Voyager model environment
Cube User Interface Works with regional network in Cube Voyager Common scripting language and data
requirements First full release of Cube Avenue works with
latest version of Cube Voyager (4.1)
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Cube Avenue Technical Facts
Unit of travel is the “packet”– Represents some number of vehicles traveling from
same Origin to same Destination
Link travel time/speed is a function of– Link capacity– Queue storage capacity– Whether downstream links “block back” their queues
Link volumes are counted in the time period when a packet leaves the link
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Houston Evacuation DTA: Existing models and data
Tool is an add-on to existing H-GAC travel demand model in Cube– Basic highway networks from regional model– Adjustments to network based on event parameters– Network modifications may vary across time
horizon of event• Flooding of low-lying links• Failure/closure of facilities• Reversal of freeway lanes
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Houston Evacuation DTA Networks
Network from regional model Coding adjustments
– Centroid adjustment in downtown– Capacity and Storage Adjustment– Network Simplification
• Link Reduction• Centroid Connectors
– Turning Movements/Prohibitions– Intersection definition
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72 x 1-hour Assignments?
Entire 3-day storm approach window Individual 1-hour slices allows network changes What do we mean by 1-hour slice?
– 1-hour period of “analysis” from which results are reported
– Additional 1+ hour period of warmup (“pre-load”) whereby trips are loaded onto the network
– Ensures that trips in analysis period see and respond to full-load conditions
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Houston Evacuation DTA Challenges
Long trip lengths Memory Limitation Ramp and freeway coding Long Running Time
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Challenges: Long Trip Lengths
Houston is a huge region Background trips >1 hour not uncommon In evacuation conditions,
– ~95% of trips are longer than 1 hour– ~45% of trips are longer than 3 hours
Longer “pre-load” to ensure maximum number of trips have a chance to complete their trip in the analysis period.
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Long Evacuation Trip Lengths
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350
Travel Time (minutes)
Eva
cuat
ion
Tri
ps
0
10
20
30
40
50
60
70
80
90
100
Cu
mu
lati
ve P
erce
nt
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Challenges: Memory Limitations
Large dimensions of problem size Windows XP maximum memory for a
single process is 2GB Limits the number of pre-load hours and
iterations possible Network Simplification, reduce pre-load
period and iterations Wait for Windows “Vista” 64-bit
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Challenges: Ramp and Freeway Coding
Texas style slip-ramps Networks are coded with Freeways and
Frontage roads separated Link coding codes through lanes but not
accel/decel lanes Storage capacity not accurately reflected
by default coding
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Ramp and Freeway Coding
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Challenges: Ramp and Freeway Coding
Ramp storage capacity as-coded was minimal
Queues from downstream intersections Queues block back onto mainline freeway
lanes too frequently All mainline lanes have equal impact upon
queue blockback Make ramp storage capacity large
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Challenges: Long Running Times
Simulations take hours to run one hour of simulation (with pre-load)
X hours x 72 time periods => Long time Makes it difficult to test different tweaks Faster computer (processor/memory/hard drive) Wait for Windows “Vista” (64-bit) Capability to run selected hours only Cube Cluster distributed processing (future)
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Next Steps/Still to Do
Refine application and verify software performance
Code intersections more explicitly Integrate attenuation of background demand Integrate evacuation demand Validate against known event speed/time data Re-visit time-of-day factors
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Thank You
Peter MazurekDirector of Consulting ServicesCitilabs, Inc222 Prince George St, Suite 100Annapolis, MD 21401(410) [email protected]
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Current Progress Developed hourly trip tables for normal
daily traffic Developed Rita evacuation demand trip
table for entire 72-hours period Validating normal daily scenarios
– Show directional speed difference in peak period
– VMT and speeds Simplified network
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Future Steps
Modify trip generation and distribution models to adopt different evacuation scenarios
Integrate normal daily and evacuation traffic to replicate Rita scenario
Coding traffic signals and other traffic control devices
Allow policy and environmental factors to change the network at specified time
Randomly generate accidents
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