Applying Meso-scopic Simulation to Evacuation Planning for the Houston

Region

Chi Ping Lam, Houston-Galveston Area Council

Colby Brown, Citilabs

Chris Van Slyke, Houston-Galveston Area Council

Heng Wang, Houston-Galveston Area Council

Acknowledge

Special thanks to Alan Clark, the executive director of Houston-Galveston Area Council, for his supports for this project

Special thanks to Matthew Martimo for his vigorous testing on DTA assignments

Citilabs and Texas Transportation Institute

Outlines1. Background

2. Introduction to Meso-scopic Assignment

3. Improve Model Performance

4. Re-generate Real World Scenario

a) Detect Network and Demand coding issues through normal daily run

b) Evacuation results

5. Sensitivity for Different Evacuaton Scenarios

6. Next Steps

Background

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

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

Goal of this model

Re-generate the Rita evacuations Provide evacuation demands Estimate traffic volumes and delays Sensitive to various scenarios and plans

Project Management Joint project of Citilabs and H-GAC H-GAC and TTI develops the trip table (presented in last

planning conference) Citilabs provides the Dynamic Traffic Assignment

software and constantly enhancing it, partly based on our recommendations. (Special thanks to Matthew Martimo)

Citilabs delivers a draft version of the model in summer 2008

H-GAC is currently on validate the results and enhance the models to our needs.

Introduction to

Mesoscopic Assignment

Why not use traditional methods?

Why NOT use traditional (Static) assignment?– Cannot model impact of queues to adjacent links– Not conducive to time-series analysis– Allow volume over capacity

Why NOT use traffic micro-simulation?– Usually for smaller scale project – Study area of interest too large and complex– Too much data – Too many uncertainties to model accurately– Likely crashed in regional scale

MesoScopic Models

Possible to quickly analyze larger areas with a more detailed model which overcomes the pitfalls of the macroscopic travel demand models.

– Takes into account intersection configurations and controls

– More detailed estimates of delay, travel time, and capacities

– Enforces capacity limitations and the effects of queues ‘blocking back’

– Models flow curves and changing demand throughout an analysis period

Transportation modeling tools

Macroscopic Modeling

Mesoscopic Modeling

Microscopic Modeling

What is mesoscopic model?

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

Track movement of individual packets over time Flow-based calculation A bridge between traditional region-level static

assignment and corridor-level micro-simulation

ImproveModel

Performance

Performance Issues Initial testing were dodged with problems

– Long running time: Takes days to complete the model– the model may crash before it completes– Results not make sense: calibration and validation needed

Four major causes of the slow and unreliable performance issues– Large number of zones and packets– Overloading due to too few iterations and path

choices– Network and demand coding issues

Packets

A packet is a group of vehicles coming from the same origin to the same destination within a time period

The basic simulation unit in the dynamic traffic assignment

Each packet can hold any number of trips Since a packet could hold more than one

vehicle, simulating packets should reduce run time and memory consumption

Memory Constraints 32 bit computing (Windows XP) limits a single

process to access at most 2GB computer memory

When a process tries to use more memory than is available in RAM, things slow WAY down.

Memory is consumed to track movements of packets currently on the network. A packet which has not begin its trip or reaches its destination will not consume memory

Limit the Number of Packets 2GB can simulate more than Six Million

packets at anyone time. There are only around 30 million trips total if each packet holds one trip, then six million

packets should be enough for the simulation The problems are because

– too many fraction packets holding less than one trips

– Unrealistic congestion due to network coding and iteration issues

Hourly Trip Tables The hourly trip tables are

calculated as

#daily trips * hourly factor In this large network, many zone-

pairs are with small large number or even fraction number of daily trips

Dividing the daily trips to hourly trip tables multiply number of packets

Hour Trips

1 0.06

2 0.06

3 0.06

4 0.06

5 0.1

… …

Daily 1

Total 24 Packets

Hourly Integerization

Back to example, if there is only one daily trip from zone A to zone B, it is necessary to generate a fractional trip/packets for every hour.

The hourly factor could be viewed as a hourly probability function of when the trip begins

Then randomly assign the integer trip based on the hourly probability function, hence reduces number of trips and packets

Probabilistic Hourly Factor

Total 12 Packets

Aggregate Zones Half to two-third of total running

time is spent on path-building Reducing number of zones could

reduce running time Many zone pairs have fraction

daily demand (less than 1 trips). Zonal aggregation could combine

those fraction trips to more than 1 and further reduce number of packets

We aggregate our 3000 zones to less than 600 zones

Number of Iterations and maximum path choices

In our model, evacuation begins in a regular day. Therefore the evacuation model assigns regular day traffic in early evacuation periods.

There are 8 iterations and maximum 4 route choices for a zone pair within the same hour. We pick small number of iterations and route choices to reduce run time.

It turns out that there are not enough iterations and route choices to let packets to learn all possible path

Therefore the traffic does not spread out enough, overloading certain routes.

Example: 7am Assignment

In iteration 1, 89000 packets remain at 9 am

In iteration 10, 2000 packets remain at 9 am

Insufficient number of iterations could create artificial congestion

Sufficient number of iterations is necessary to distribute the traffic evenly, and better calibration and validation

Number of Iterations and maximum path choices

The final model settle on 30 iterations and maximum 12 route choices for optimum run time and assignment results.

Surprising, increase path choice reduces run time as well.

Model Run Time

Hourly Integration and zone

aggregation?

Single or multi-class regular day

assignment?

Number of Iterations

Maximum number of

path choiceRun Time

No Single ?? ?? A few days

Yes Single 8 4 ~9 hours

Yes Single 20 4 16-20 hours

Yes Multi 20 4 38-42 hours

Yes Multi 30 12 36-46 hours

Standard BPR Curves The most common volume-delay curves in 4-step model It is intended to use free flow speed and design capacity (LOS C) The design capacity is less than maximum capacity (LOS E) The speed at V/C=1 is around 15% less of the free low speed H-GAC travel demand model applies standard BPR function with

LOS C speed and maximum capacity to forecast more accurate demand

DTA requires a better speed-capacity relationship with free flow speed and maximum capacity. Standard BPR function does not fit DTA

Other researchers has suggested other BPR parameters or functions to model volume-delay more accurately

BPR Curves Alan Horowitz proposes

another set of BPR functions which has much lower speed at maximum capacity

We picks two set of BPR parameters to model freeway and local streets differently.

Speed at local streets deteriorates faster

The speed in our BPR functions decrease in small V/C. It exaggerate speed decrease but make the path-finder more sensitive to volume changes.

Summary of Improvement

The initial model suffer from the out-of-memory issue and slow performance

Because there are many packets on the network Adopt random hourly trip integerization to reduce

number of packets Aggregate 3000 zones to less than 600 zones Select appropriate number of iterations and maximum

path choices allowed to produce reasonable assignment results with reasonable running time

Re-examine volume-delay function

Re-GenerateReal WorldScenarios

Real World Scenarios

There are two real world scenarios:– Year 2005 Regular Day Scenario (no evacuation occurs)– Year 2005 Rita Evacuation Scenario

We could like to validate the daily volume of the regular day scenario. It is unknown in what degree the zonal aggregation will impact the accuracy of validation

For the Rita evacuation scenario, it is very difficult to validate as most traffic data are not available. However, the model should generate some

Regular Day Scenario

Only regular day traffic are loaded The daily trip table is split to 24 hourly trip

tables by the hourly integerization method During this process, many network coding

are discovered

Network Coding Issues The model borrows the network from the regional travel

demand model, which allows volume over capacity, and does not model queuing.

Regional travel demand model is a planning tool which set its first priority on demand; it allow links with volume-capacity ratio over 1 to indicate high demands.

Some links with V/C ratio over 1 could be caused by network coding issues. Those network coding issues are hidden in the regional network but are exposed in mesoscopic assignments

Turning lanes and auxiliary lanes are not coded in the network, but they are important to provide capacity to the capacity-sensitive mesoscopic assignment.

Galleria at 9pm Galleria is a

shopping and employment center

Even though it is congested, it is not as congested as the model suggested

The congestion spilt back to impact a big area Red color = less than 10 mph at 9pm

Network Checking

After checking with aerial photo and Google Earth, we add auxiliary lanes on the freeway intersection and turning lanes on frontage roads

Galleria after adding auxiliary and turning lanes

After adding the auxiliary lanes and turning lanes, the system wide congestion at 9 pm disappeared.

There are still minor congestion due to busy intersection or uneven centroid loadings.

Comparing Mesoscopic and Macroscopic Assignments

Overall VMT decrease slightly

In static assignment, most trips takes the shortest CC out. In DTA, more trips to use the longer CC to bypass congestion.

Lower volumes on the local streets as packets use long CC in aggregated zone structure to bypass congestion

Roads Static DTA %Diff

Centroid 25,606,062 26,265,858 3%

Freeway + HOV

53,052,062 54,822,016 3%

Toll 5,103,008 4,907,261 -4%

Local 55,424,000 51,537,1845 -7%

All 139,185,160 137,532,320 -1%

VMT Summary

Validate Regular Day Traffic

The goal is not to match traffic count very well, but to provide impact of regular day traffic during early evacuation period.

In process of screen line analysis. The validation will not be very close to traffic

count because of the aggregated zone. Eventually we will validate the regular day

traffic in our full-blown zone structure as in our regional travel demand model.

Rita events

It was 6-days evacuation We choose the 3 consecutive days out of the 6 days

when 90% of evacuations and congestion occur In first 1.5 days, most evacuations originate from

mandatory zones, and congestion is less severe and contained locally. People in non-mandatory zones are traveling in regular pattern.

In latter 1.5 days, evacuations originate from every part of the region, and congestion is spread all over the region

Evacuation District

The 3 coastal districts, in red and orange colors, are mandatory evacuation zones

The other 3 districts, in yellow and green colors, are defined by their distance from the coast. It is non-mandatory evacuation area.

Trip Purposes

There are three kind of trips in the Rita events Evacuation traffic

– Almost 90% leaves the region– Mostly follow evacuation route or freeway/Highway

because they do not have knowledge of local routes of entire H-GAC region

Regular day traffic– Routine daily trip

Evacuation Traffic Almost 90% leaves the region Mostly follow evacuation routes or freeway/Highway

because they do not have knowledge of local routes outside their areas

Regular Day Traffic In first 1.5 days of Rita

evacuation, most people were making routine daily trips

Even in late evacuation period, some people still make routine daily trips in non-mandatory evacuation districts.

User-equilibrium nature and could avoid congested routes

Non-evacuate Special Trips

Non-evacuating residents prepare for coming disaster

Go to hardware store, collect foods visit friends/relatives

Generally short trips Like regular day traffic,

user equilibrium nature No survey data

Mixing those trip purposes The evacuation model must assign all three trip

purposes at the same time Regular day traffic and non-evacuate special trips are

combined to one class as both are user-equilibrium natures

For evacuation traffic,– Less number of iterations (less path choices)– Introduce local deter factor in the cost function to mimic their

unfamiliarity to local arterials. In the cost function, this factor multiplies the travel time of local streets on non-evacuation routes.

Validate Evacuation Model

Freeway speed data collect from Automatic Vehicle Identification technology

Traffic count at a few locations The data does not cover entire region, and

there are people question their accuracy under such slow-moving traffic

9/22/2005 10am

Validation Progress

Current model shows significant congestion on outbound congestion

The modeled congestion is more severe than the collected data suggests on most northbound corridors

We are checking the demands and network issues.

Our goals afterValidation…

Evaluating planning scenarios!

We could like to evaluate different evacuation strategies – Contraflow lanes– Ramp closures– Utilize additional evacuation routes– Open toll lanes to public– Signal

Run a future year scenario With computer power improving, eventually

run a model with full 3000 TAZ

LessonsLearned

Lesson 1: People It takes time for modelers to realize that meso-scopic

assignments is not macro-scopic or micro-scopic assignments

For travel demand modelers:– It is too sensitive to capacity!– Randomization is scary …

For micro-simulation engineers:– Capacity is artificial – Vehicles should be still moving in queue …

There are months of confusion and arguments before we accepted the strength and weakness of meso-scopic models.

Lesson 2: Data Preparation The project could be more efficient if we prepare the

data better Check all networks are coded correctly, especially for

congested area, bottlenecks, links with volume over capacity in the static model

Identify any areas which the demands is over the supply Get some hourly traffic count or survey to create or

update hourly split factor Hourly count and speed data are good for validation

Lesson 3: Start with something small

If there is a mulligan, we could rather start from a sub-area. For a big network,

– Too many hidden network and demand issues– Difficult to diagnosis – Wait a long time to get results

Recommend approach: start with a small, congested area– Any network or demand issue in congested area are critical

because the impact will spread out – Calibrate the model in this sub-area

Next Steps

Complete the validation! Awaiting the next version of Cube Avenue,

which Citilabs promise a more much efficient algorithm

Use details zone instead of aggregated zones.