Intelligent Traffic Signal Control with Deep Reinforcement Learning at Single Intersection

In this paper, we apply the Proximal Policy Optimization (PPO) algorithm in intelligent traffic signal control at a single intersection with eight lanes and four signal phases. The optimization goal is to minimize the average waiting time of vehicles so as to improve the traffic efficiency of the intersection. Extensive experiments are conducted in Simulation of Urban MObility (SUMO) to evaluate the performance of the proposed algorithm, and compare it with other classic algorithms including Deep Q-network (DQN), Advantage Actor Critic (A2C) and Fixed Time. Simulation results show that the proposed PPO algorithm outperforms the others under various traffic scenarios to different extent. The performance gain is significant under unbalanced traffic where one direction is saturated while the other is not, and becomes marginal when all the directions are saturated or unsaturated. PPO also demonstrates good portability and robustness over time-varying traffic patterns, while implies it could be a preferable option for implementation in real world intelligent traffic signal control systems.