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Update on Developing Evacuation Model using Dynamic Traffic Assignment. ChiPing Lam, Houston-Galveston Area Council Matthew Martimo, Citilabs. Review last Presentation. During Rita Evacuation, evacuation routes were very congested. “Crawling parking lot.”

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update on developing evacuation model using dynamic traffic assignment

Update on Developing Evacuation Model using Dynamic Traffic Assignment

ChiPing Lam, Houston-Galveston Area Council

Matthew Martimo, Citilabs

review last presentation
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
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
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
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
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
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
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
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
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
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
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
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
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.

packets
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
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 trips
Non-integer Trips
  • 10 million trips
  • Due to non-integer trips, the number of packets ends up being MUCH larger.

Example: Drive Alone Free Trip Table

reduce number of non integer trips 1
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
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
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
changes to the software
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
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
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
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

volume delay curves 2
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 simulation
Mesoscopic Simulation

When 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
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
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
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
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
calibration
Calibration

Now in Calibration Phase of a normal day assignment

Identify (and fix) problem spots in the network using two approaches:

A static assignment to check for areas were Volume greatly exceeds capacity

Run DTA on sub-areas for faster run time and easier problem identification, particularly network problem.

conclusion discovery
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
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
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?