Statistical mechanics-inspired framework for studying the effects of mixed traffic flows on highway congestion

摘要

Intelligent vehicles equipped with adaptive cruise control (ACC) technology have the potential to significantly impact the traffic flow dynamics on highways. Prior work in this area has sought to understand the impact of intelligent vehicle technologies on traffic flow by making use of mesoscopic modeling that yields closed-form solutions. However, this approach does not take into account the self-organization of vehicles into clusters of different sizes. Consequently, the predicted absence of a large traffic jam might be inadvertently offset by the presence of many smaller clusters of jammed vehicles. This study - inspired by research in the domain of statistical mechanics - uses a modification of the Potts model to study cluster formation in mixed traffic flows that include both human-driven and ACC-enabled vehicles. Specifically, the evolution of self-organized traffic jams is modeled as a non-equilibrium process in the presence of an external field and with repulsive interactions between vehicles. Monte Carlo simulations of this model at high vehicle densities suggest that traffic streams with low ACC penetration rates are likely to result in larger clusters. Vehicles spend significantly more time inside each cluster for low ACC penetration rates, as compared to streams with high ACC penetration rates.