1. Comparing Dynamic Traffic Assignment Approaches for Planning
Ramachandran Balakrishna
Daniel Morgan
Qi Yang
Caliper Corporation
12th TRB National Transportation Planning Applications Conference, Houston, Texas
20th May, 2009

2. Outline Introduction Motivation DTA comparison methodology
TransModeler 2.0 overview
DTA in TransModeler 2.0
Empirical tests
Conclusion

3. Introduction Within-day dynamics: I-405, Orange County, CA
[Source: PeMS on-line database]
Temporal variability
Complex interactions of network demand
Aggregation error

4. 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.

5. 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

6. 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

7. 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

8. 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

9. DTA in TransModeler 2.0 (contd.)
Simulation-based DTA
Feedback methods
Path flow feedback
Link travel time feedback
Fidelity
Microscopic
Mesoscopic
Macroscopic
Hybrid

10. Simulation-Based DTA Framework
Path flow averaging

11. Simulation-Based DTA Framework (contd.)
Link travel time averaging

12. Simulation-Based DTA Framework (contd.)
Averaging method
Choice of averaging factor
Method of Successive Averages (MSA)
Polyak
Fixed-factor

13. Empirical Tests Columbus, Indiana 6630 nodes 8811 links 85 zones
AM peak period
7:00-9:00
~42,000 trips

14. 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

15. Empirical Tests (contd.)
DTA
Feedback method: MSA
Path flow averaging
Link travel time averaging
Model fidelity
Microscopic
Mesoscopic
Four experiments

16. Empirical Tests (contd.)
Microscopic DTA results

17. Empirical Tests (contd.)
Mesoscopic DTA results

18. Empirical Tests (contd.)
Feedback with path flows

19. Empirical Tests (contd.)
Feedback with link travel times

20. 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

21. 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