An equilibrium-seeking search algorithm for integrating large-scale activity-based and dynamic traffic assignment models

TL;DR

This paper introduces an iterative, decoupled method to integrate activity-based and dynamic traffic models, efficiently reaching equilibrium in large-scale urban traffic simulations with limited iterations.

Contribution

It presents a novel decoupled iterative approach that allows ex-post integration of existing models to find equilibrium between demand and supply.

Findings

Achieves equilibrium within 15 iterations for a city of 400,000 inhabitants.

Reaches a measure of error below 10% in limited iterations.

Validated equilibrium results against baseline distributions.

Abstract

This paper proposes an iterative methodology to integrate large-scale behavioral activity-based models with dynamic traffic assignment models. The main novelty of the proposed approach is the decoupling of the two parts, allowing the ex-post integration of any existing model as long as certain assumptions are satisfied. A measure of error is defined to characterize a search space easily explorable within its boundaries. Within it, a joint distribution of the number of trips and travel times is identified as the equilibrium distribution, i.e., the distribution for which trip numbers and travel times are bound in the neighborhood of the equilibrium between supply and demand. The approach is tested on a medium-sized city of 400,000 inhabitants and the results suggest that the proposed iterative approach does perform well, reaching equilibrium between demand and supply in a limited number of iterations thanks to its perturbation techniques. Overall, 15 iterations are needed to reach values of the measure of error lower than 10%. The equilibrium identified this way is then validated against baseline distributions to demonstrate the goodness of the results.