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

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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

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