An Equilibrium-Seeking Search Algorithm for Integrating Large-Scale Activity-Based and Traffic Assignment Models

This paper proposes an iterative methodology to integrate large-scale behavioral activitybased models with mesoscopic traffic assignment models. The proposed approach fully decouples the two parts, allowing the ex-post integration of multiple models 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 5%. The equilibrium identified this way is then validated against baseline distributions to demonstrate the goodness of the results.