update on developing evacuation model using dynamic traffic assignment

Update on Developing Evacuation Model using Dynamic

Traffic AssignmentChiPing Lam, Houston-Galveston Area CouncilMatthew Martimo, Citilabs

Review last Presentation During Rita Evacuation, evacuation routes

were very congested. “Crawling parking lot.”

H-GAC was asked to develop a tool for evacuation planning.

Challenges Large network and demands Long trip length and travel time Interaction between evacuation and non-

evacuation traffic Network changes during evacuation period

(eg: contraflow, HOV and toll open to public)

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)

H-GAC’s 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

Review – Why 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

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

Progress on Last Presentation Based on TXDOT survey, develop trip

generation model Using a simplified and relax gravity model

to assign evacuation demands Develop hourly factors for evacuation

traffic and normal traffic reduction

Progress on Last Presentation(2) Ramp Storage Adjusted to account for

storage lane and through lane on freeway, to avoid over-estimate backup

Network simplification to save memory Single class assignment 72 1-hour assignment to account for

network changes

Computer Limitations 32 bit computing (Windows XP) limits how

much computer memory can be accessed by a single process to 2GB.

Initially the problem size was requiring more than 2GB of memory and was failing altogether.

Previous suggestion: Simplified Network to reduce memory requirement

Overview for this presentation Problem Size

– Greater Houston-Galveston Metropolitan Area– 72 hour simulation of evacuating vehicles

Initially strained the available computing resources

Mesoscopic modeling versus standard Macroscopic Travel Demand Modeling

Simplified Network Abandon Only Major arterials, highways, and freeways

remained in the simplified network. In retrospect, this was a VERY bad idea…

because of the nature of Mesoscopic Simulation… This will be described in a few minutes.

In fact, the more detail available in the network, the better. We are now modeling with the full travel demand modeling network.

Multi-Class Assignment Single class assignment remove some of the

ability of the model to properly replicate flows seen on the roadways

Making calibration more difficult. Now model multi-class assignment similar to the

static model, each with their own path sets. Drive alone free (No HOV, Toll, HOT) Drive alone pay (No Toll) 2 person free (No Toll, HOT) 3+ person free (No Toll) Share ride pay (allow everything)

Increase Number of Iterations Originally zero to 1 iteration (similar to AON

assignment) Vehicles jam to the AON route, cause

extremely long travel time and consume more computer memory

Ill-conceived as with each subsequent iteration, the vehicles learn more about possible routes and their environment.

With each subsequent iteration, the model is more stable, reliable, and easier to calibrate.

Number of Iteration vs Travel time for Single hour assignment

Packets Network are simulated in packets. A group of trips with same origin, destination,

and start time. Treated as if a single unit Each packet can hold any number of trips. Tracking and simulating these individual

packets is what consumes the memory. 2GB can simulate more than Six Million packets at anyone time.

Limit the Size of Packets Originally, the maximum size of packet is

ten vehicles or less Large size is to reduce number of packets;

to consume less memory With software upgrade and increase

iteration, now is one vehicle trip per packet Reduce number of non-integer trips

Non-integer TripsExample: Drive Alone Free Trip Table

10 million tripsDue to non-integer trips, the number of packets ends up being MUCH larger.

Reduce Number of Non-Integer Trips (1)

Alternative 1: traditional bucket rounding for each hourly demand

Add fraction trips across column, and assign a trip when the sum of fraction equals to or exceeds 1

Does not reserve column (destination) total, which is bad as evacuation traffic is concentrated on a few external destinations

Reduce Number of Non-Integer Trips (2)

Alternative 2: Cross-time bucket rounding Summing across time rather than column,

hence preserve origin-destination total Too little traffic on early hours because for

many origin-destination, sum of early hour trips is less than 1 (no packet assigned)

Probabilistic Integerization (1) For each origin-destination pair, produce

probability distribution based on hourly demands

Simulate integer trip based on probability Sum of Daily Trips for each origin-

destination reserves, and early-hours are assigned with adequate traffic

Probabilistic Integerization(2)

Changes to the Software To properly simulate network changes,

such as reversible HOV facilities, contra flow lanes and etc, the following changes were made to the software: Ability to turn facilities on and off during the

simulation Ability change the capacity of facilities during

the simulation. Ability to animate packet during the simulation

Changes to the Methodology Previously, break down the 72-hours

evacuation into 72 single hour assignments to allow network changes

Now simulate the entire 72 hours of evacuation in one long simulation, and turn on contraflow lane or reversible HOV in the middle of simulation

Reduces run time from 3 days to half days

Cluster Speed up the simulation by distributing the

work to more than one processors Now groups of computers can work on

finding the best path for each packet (one major task).

While others work on simulating the packets as they become available (the other major task).

Volume Delay Curves In macroscopic assignment, assigned

volume can exceed capacity. The Volume-Delay curves were adjusted

to limit the ability of the model to assign more trips than the available capacity.

The speed is too high comparing to reality

Example: Freeway curve

Volume Delay Curves(2) On contrast, DTA does not allow volume to

exceed capacity. Therefore, speed should decrease sharply

when volume approaches capacity Standard speed-capacity curve from

Highway Capacity Manual replaces the volume delay curve in regional demand model

Mesoscopic SimulationWhen Compared with Macroscopic Assignment:

– Vehicles take up space and progress through the network.

– Capacity strictly limits the rate at which vehicles progress.

– Available Storage strictly limits the number of vehicles that can occupy a link.

– If vehicles cannot progress they must wait. – A full link blocks ‘back’ and will impact upstream

links

Theorem of One Bad Link In static assignment, volume on one link may

over capacity and does not impact adjoining roadways.

In the mesoscopic simulation, when a link is over capacity, incoming vehicles must queue on upstream links to wait for their turn

A link with extremely high v/c ratio could cause serious congestion on adjacent links

Impacts on Mesoscopic Assignment

Example of a centroid connector between a mall (represented by a TAZ) and a frontage road … It is the only centroid connector of that TAZ.

Frontage road has capacity of 1444 vph , but than 6000 trip demands during 8am…

tens of thousands of trips sitting on the upstream links blocking all the roadways.

Solution: adding more centroid connectors

Network Clean up Incorrect Network coding may cause

illogical path. Its impact could be very severe in mesoscopic assignment

Missing turn prohibition Incorrect distance coded Lazy coding: one coded link to substitute

many links in real world

Impact of Incorrect Distance The Frontage road

coded as 0.2 miles instead of 1.1 miles

Freeway through traffic diverts to frontage road

Subsequent time slices showing illogical backup on other links

Example of Lazy codingOne link to represent all direct ramps

Detail CodingLazy Coding

Calibration Now in Calibration Phase of a normal day

assignment Identify (and fix) problem spots in the

network using two approaches:1.A static assignment to check for areas

were Volume greatly exceeds capacity2.Run DTA on sub-areas for faster run time

and easier problem identification, particularly network problem.

Conclusion - Discovery Sufficient number of iterations is required

to eliminate long travel time and nonsense backup

Clean network is necessary High V/C ratio link in static model will

cause severe congestion on adjoining links in DTA assignment

HCM curve is more suitable for DTA than volume delay curve for regional model

Conclusion - Progress Develop probabilistic distribute to

aggregate and to simulate fraction trips to integer trips

Replaces the “simplified” network with full network

Multi-class assignment adopted A single 72-hours simulation substitute 72

one-hour assignment, saving run time

Continuing Challenges Calibrate the normal day scenario Mesh evacuation traffic with non-

evacuation traffic, as these two types of traffic behave very different.

Code traffic signals More network cleanup may be necessary Trip Table adjustment?