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Comparing Dynamic Traffic Assignment Approaches for
Planning
Ramachandran BalakrishnaDaniel Morgan
Qi Yang
Caliper Corporation
12th TRB National Transportation Planning Applications Conference, Houston, Texas
20th May, 2009
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Outline
• Introduction• Motivation• DTA comparison methodology• TransModeler 2.0 overview• DTA in TransModeler 2.0• Empirical tests• Conclusion
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Introduction• Within-day dynamics: I-405, Orange County, CA
0
2000
4000
6000
8000
10000
12000
14000
0 50 100 150 200 250 300 350
Time of Day
Flo
w (
veh
/ho
ur)
Hourly Flows
5-Min Flows
[Source: PeMS on-line database]
• Temporal variability– Complex interactions of network demand– Aggregation error
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Introduction (contd.)
• Static traffic assignment– Cannot capture detailed within-day dynamics– Does not handle capacity constraints, queues
• Produces unrealistic results (e.g. flow >> capacity)
• Dynamic traffic assignment (DTA)– Models temporal demand, supply variations
• Uses short time intervals, usually 5-30 minutes
– Captures capacity constraints, queues, spillbacks
– Superior to static for short-term planning• Evacuation & work zone planning, dynamic tolls,
etc.
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Motivation• Different types of DTA
− Analytical− Simulation-based (micro, macro, meso)
• Tradeoffs perceived between realism, running time
• Methods are often chosen based on available computing resources
• Objective comparison of different methods is lacking
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DTA Comparison Methodology
• Objective: User equilibrium (UE)− Dynamic extension of Wardrop’s principle− Same impedance (e.g. travel time) for all
used paths between each OD pair, for a given departure time interval
• Test DTAs on common platform and dataset
• Measure and compare convergence− Relative gap− Convergence rate
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TransModeler 2.0 Overview
• Simulates urban traffic at many fidelities− Microscopic (car following, lane changing)− Mesoscopic (speed-density relationships)− Macroscopic (volume-delay functions)− Hybrid (all of the above)
• Employs realistic route choice models• Handles variety of network
infrastructure− Signals, variable message signs, sensors,
etc.
• Simulates multi-modal, multiple user classes
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DTA in TransModeler 2.0
• Analytical (Planner’s DTA)− Based on Janson (1991), Janson & Robles
(1995)
• Simulation-based DTA− Feedback approach− Iterates on simulation output until
convergence
• All DTAs are run on same network
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DTA in TransModeler 2.0 (contd.)
• Simulation-based DTA
− Feedback methods• Path flow feedback• Link travel time feedback
− Fidelity• Microscopic• Mesoscopic• Macroscopic• Hybrid
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Simulation-Based DTA Framework
• Path flow averaging
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Simulation-Based DTA Framework (contd.)• Link travel time averaging
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• Averaging method
• Choice of averaging factor− Method of Successive Averages (MSA)− Polyak− Fixed-factor
Simulation-Based DTA Framework (contd.)
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Empirical Tests
• Columbus, Indiana− 6630 nodes− 8811 links− 85 zones
− AM peak period• 7:00-9:00• ~42,000 trips
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Empirical Tests (contd.)
• Static assignment
− Relative gap• 50 iters: ~0.008• 100 iters: ~0.006 • 2000 iters:
~0.0005
− Run time• 50 iters: ~36 sec• 100 iters: ~1 min• 2000 iters: ~24
min
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Empirical Tests (contd.)
• DTA− Feedback method: MSA
• Path flow averaging• Link travel time averaging
− Model fidelity• Microscopic• Mesoscopic
• Four experiments
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Empirical Tests (contd.)
• Microscopic DTA results
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Empirical Tests (contd.)
• Mesoscopic DTA results
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Empirical Tests (contd.)
• Feedback with path flows
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Empirical Tests (contd.)
• Feedback with link travel times
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Conclusion
• Static assignment is fast with known properties, but does not capture dynamics
• Simulation-based DTA is more realistic but slower and harder to analyze
• Travel time feedback appears to be faster than path flow averaging for simulation-based DTA
• Tests on more networks are required
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Analytical DTA Framework
• Planner’s DTA− Based on Janson (1991), Janson & Robles
(1995)
− Bi-level, constrained optimization • Outer: consistent node arrival times• Inner: User equilibrium for given node arrival
times
− Extended by Caliper:• Spillback calculations• Stochastic user equilibrium• Better travel times
− Reasonable results on large planning networks