  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.
