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

Outline

• Introduction• Motivation• DTA comparison methodology• TransModeler 2.0 overview• DTA in TransModeler 2.0• Empirical tests• Conclusion

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

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.

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

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

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

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

DTA in TransModeler 2.0 (contd.)

• Simulation-based DTA

− Feedback methods• Path flow feedback• Link travel time feedback

− Fidelity• Microscopic• Mesoscopic• Macroscopic• Hybrid

Simulation-Based DTA Framework

• Path flow averaging

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

• Averaging method

• Choice of averaging factor− Method of Successive Averages (MSA)− Polyak− Fixed-factor

Simulation-Based DTA Framework (contd.)

Empirical Tests

• Columbus, Indiana− 6630 nodes− 8811 links− 85 zones

− AM peak period• 7:00-9:00• ~42,000 trips

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

Empirical Tests (contd.)

• DTA− Feedback method: MSA

• Path flow averaging• Link travel time averaging

− Model fidelity• Microscopic• Mesoscopic

• Four experiments

Empirical Tests (contd.)

• Microscopic DTA results

Empirical Tests (contd.)

• Mesoscopic DTA results

Empirical Tests (contd.)

• Feedback with path flows

Empirical Tests (contd.)

• Feedback with link travel times

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

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