Update on Developing Evacuation Model using Dynamic Traffic Assignment

1
Update on Developing Evacuation Model using
Dynamic Traffic Assignment

• ChiPing Lam, Houston-Galveston Area Council
• Matthew Martimo, Citilabs

2
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.

3
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)

4
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)

5
H-GACs 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

6
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

7
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

8
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

9
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

10
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

11
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

12
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.

13
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)

14
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.

15
Number of Iteration vs Travel time for Single
hour assignment
16
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.

17
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

18
Non-integer Trips

• Example Drive Alone Free Trip Table

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

19
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

20
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)

21
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

22
Probabilistic Integerization(2)
23
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

24
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

25
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).

26
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

27
Example Freeway curve
28
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

29
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

30
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

31
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

32
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

33
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

34
Example of Lazy codingOne link to represent all
direct ramps
Detail Coding
Lazy Coding
35
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.

36
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

37
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

38
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?