Developing Statewide Evacuation Model Chi Ping Lam, Houston-Galveston Area Council Chris Van Slyke,...
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Transcript of Developing Statewide Evacuation Model Chi Ping Lam, Houston-Galveston Area Council Chris Van Slyke,...
Developing Statewide Evacuation
Model
Chi Ping Lam, Houston-Galveston Area Council
Chris Van Slyke, Houston-Galveston Area Council
Heng Wang, Houston-Galveston Area Council
Outlines
1. Background
2. Phases for statewide evacuation model
3. Re-generate Real World Scenario
a) Detect Network and Demand coding issues through normal daily run
b) Evacuation results
4. Sensitivity for Different Evacuaton Scenarios
5. Next Steps
Background
Motivation
In September 2005, Hurricane Rita landed east of Houston
Over 1 million people attempted to evacuate from the eight county region
Severe congestion as a results
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
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 – an evacuation model
Phases for statewide
evacuation model
Phases
Phase 1: Develop evacuation model on our 8-county MPO network– To model how well the transportation system could
move evacuee outside our region– 90% completed
Phase 2: Expand to statewide network– Model impacts from outside the MPO region– Provide a more complete evacuation experience– Early stage
Limitation on Phase 1 Model Around 90% of Rita evacuation trips travel outside of
MPO region. The queues extended far away our region. The external stations are treated as “destinations” in
phase 1 trip distribution. Those external stations are not real destination.
Some known bottlenecks are outside the MPO network, and the traffic queued back to the MPO network. The Phase 1 model could not model the effect of the bottlenecks well.
Some evacuation policies, like contraflow lanes, extend to and impact area outside the MPO networks. Phase 1 model could not model their full impact
Outside Outside RegionRegion
Inside Inside RegionRegion
Goals of Phase 2 Model
Generate a complete evacuation trip tables Model impact of bottlenecks outside the MPO region Model policies outside the MPO region Measures congestion outside the MPO region Provide a complete picture of evacuation experience,
such as total travel time
Phase Two Processes
1. Get a copy of statewide model (in TransCAD)
2. Convert the trip tables and the network from TransCAD format to Cube Voyager format
3. Merge the statewide and regional networks and trip tables
4. Manually coding the missing bottleneck
5. Develop statewide evacuation trip tables
6. Test Run
7. Model Performance Improvement
8. Calibration and Validation
Progress on Developing
Statewide EvacuationTrip Table
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
Phase 1 Evacuation Trip Tables
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
Six Districts Districts 1-3 are
the three mandatory evacuation districts
Districts 4-6 are other part of the MPO regions, defined by its distance to the coastline
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
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
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
External Station Evacuation Attractions
Distributed attractions to other urban areas based on their population and relative accessibility
Allocated results to external stations
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
From Region-wide to state-wide trip tables
Obviously, the external stations of the MPO network are not true destinations but rather a “gate” to outside MPO destinations.
Trip generation and distributions should use real destinations
First task is to geo-coding the survey to statewide level
Geo-coding the OD
4,092 records are inputted for geo-coding ArcGIS automatically geo-code the destinations
if the destinations are cities inside Texas. Most coding errors are mis-spelling which could
be corrected Only 0.4% of records are without sufficient
information to identify the destinations
Where are people going to?Top 10 Destinations (by Transtar Survey)
Austin 9.7%Dallas 7.9%San Antonio 7.1%Houston 5.3%Louisiana 2.8%College Station 2.4%Conroe 1.6%Fort Worth 1.6%Waco 1.6%Livingston 1.5%
Hundreds of other destinations!!
Summary of Survey Findings Hundreds of destinations! 91.5% evacuation trips are in-state 16.9% evacuee change their destinations Most evacuee visit their friends or families Austin, Dallas, and San Antonio, the three largest
adjacent metropolitan area, are the top three destinations
Other major destinations concentrated on US-59, I-45, and I-34 corridors.
82% of planned in-state evacuation trips are within 4.5 hours (free flow time) from Houston
Factors of Determining Attractions
Population is the most dominant factor – Over 80% of evacuees visit family or friends– Hotel and Shelters
Coastal area are not very popular– Evacuated or closed
There is strong evidence that most evacuees disfavor long trips
Survey Trips normalized by county population
Big cities are not necessary most attractive other than number of people
Hill countries are somehow very attractive
A few outliners
Develop Trip Production Model for
Rita Scenario Our model will re-generate the Rita scenarios For trip produced inside our MPO region, use the same
production model from Phase 1 model Use the same trip table of Phase 1 model for internal-
internal trips. Very few information regarding trip produced outside our
MPO region.
Develop Trip Attractions for Rita Scenario
The considered factors:– Population– Accessibility or Distance– Distance from the coast or coastal area indicator– Potential bias factor
Initial analysis suggests linear regression model is not a good fit as population rules over other factors
Trip normalized by population may be a better variable than population
Progress on PreparingNetwork
Statewide Network
Import Texas Statewide Model (SAM) Network Include air, marine, freight rails Less roadway details inside our MPO region than
our MPO network Adequate for modeling major traffic flow Does not support every details in bottleneck, like
ramps in direct interchange or traffic light in a small town
Have to add some details to the statewide network
State Network
Merging Statewide and MPO Networks
The statewide network does not provide enough access points or local road capacity to load evacuation traffic to evacuation routes inside Houston metropolitan area
For Mesoscopic assignment, this could mean evacuation traffic get stuck in local streets, execrates local congestion while underestimates congestion on evacuation routes. Trial run of using a simplified network in Phase 1 model supports this logic.
Therefore, MPO network is required to load the evacuation traffic
Only auto links are merged. All non-auto links are deleted.
Handle Inconsistencies between the two networks
The external stations of the regional model connects to the statewide network very well (only 1 or 2 minor stations without a match)
The Statewide model and region model have different number and definitions of facility types and area types
Therefore, for the same road inside MPO region, its capacities for the statewide model and regional model are different
Use regional model setting inside MPO region
Bottleneck at US 290 @ US-36
Major bottleneck outside MPO region Cause by 1 lane direct interchange not coded in the statewide network
Other network Modifications
Adding Contraflow lanes in the network
May put toll identification to the network
Addressing Model
Running Time
Slow Test Run
Perform a test run using regular trip tables only The mesoscopic assignment complete, and only
take 96 hours to complete! Need to reduce running time for more efficient
calibration and validation process
Option1:Aggregate Zone System
SAM models has 4800 TAZ Aggregate zone. The aggregated zones cross county
boundary (zone is always within one county) The number of zone could be reduced to between 1000-
1500 zones. Reduce path-building time Avoid assign short trips as those trips are now intra-
zonal trips This should reduced the running time to less than 2 days
Option 2: Sub-area Based on the survey,
most of the evacuee did not travel west of the Hill Country area
West Texas and the Pan-handle areas could be removed from the model
Affected cities includes El Paso, Amarillo, Midland, Lubbock
Option 3: Reduce Regular-Day Traffic
One goal of model is to measure how evacuation traffic impacts regular traffic flow in destinations
The static statewide model shows serious congestion inside metropolitan area, maybe because lacks of details
Perhaps reduce regular day traffic inside urban area to compensate lacks of roads
Summary
The statewide evacuation model is the next phase of regional evacuation model.
Provide more complete pictures of evacuation experience and the impact of evacuation strategies.
Need to develop evacuation trip attraction model
Need to add crucial details to the statewide network
Need to reduce running time