A New Solution for Freeway Congestion: Cooperative Speed Limit Control Using Distributed Reinforcement Learning

Abstract

This paper presents a novel variable speed limit control system under the vehicle to infrastructure environment to optimize the freeway traffic mobility and safety. The control system is a multiagent system consists of several traffic control agents. The agents work cooperatively using the proposed distributed reinforcement learning approach to maximize the freeway traffic mobility and safety benefits. The traffic mobility objective is to maintain freeway traffic density slightly under the critical point to produce the maximum traffic volume, while the traffic safety objective is to reduce the speed difference between adjacent segments. The merits of distributed reinforcement learning are its model-free nature, and it can improve its performance continually as time goes on. The control system is developed on an open source traffic simulation software. Results revealed that compared with no control cases, the proposed system can noticeably decrease the total travel time and increase the bottleneck outflow. Moreover, the speed difference between freeway segments indicating the potential rear-end collision risk is significantly reduced. We also found that there could be more than one optimal traffic equilibrium according to different control objectives, which inspire us to design more optimal strategies in the future.