An equilibrium-seeking search algorithm for modeling the integration of supply and demand

An iterative methodology integrating large-scale behavioral activity-based models and dynamic traffic assignment ones is presented in this abstract. The main novelty lies in the decoupling of the two sides, allowing an ex-post integration of any existing model, as long as certain assumptions are met. The abstract describes a measure of error characterizing an easily explorable search space. Within it, the equilibrium state is characterized by the joint distribution of number of trips and travel times, i.e. the distribution for which trip numbers and travel times are bound in the neighbourhood of the equilibrium between supply and demand. The approach is tested on a city of 400,000 inhabitants and the results suggest that it does perform well. Overall, values lower than 10% are reached in 15 iterations. The equilibrium state is then validated against baseline distributions to demonstrate the goodness of the results.