Characteristics of Mixed Traffic Flow in Two-lane Scenario Based on Cooperative Gaming Method

Abstract

This paper aims to explore the impacts of connected and automated vehicles (CAV) on traffic flow efficiency based on in-depth microscopic simulation studies using cooperative gaming method. First, the Gipps car-following models were integrated into an improved cellular automata model to mimic the regular vehicle's driving behavior. Then, cooperative gaming method integrated with enhanced Q-learning was employed as the modeling platform for CAV, to strengthen the capability of the simulation system in realistically reproducing CAV lane changing and car following behavior. Finally, a 2-lane freeway stretch was applied to our simulations, and with extensive simulation studies we obtained some promising results. The study results suggest that the impacts of CAV are quite positive. The inclusion of CAV considerably improves traffic flow, mean speed, and traffic capacity. Such understanding is essential for research concerning CAV as well as the CAV implication for future traffic management and control.