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

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 [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

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

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