An Intelligent Traffic Signal Control System Based on Deep Reinforcement Learning

With the rapid growth of traffic demand, the urban traffic conditions are becoming more dynamic and uncertain. Traditional traffic control methods cannot cope with such complex traffic conditions, which cause traffic congestion and environmental pollution. In this paper, we propose an intelligent intersection traffic signal control method based on deep Qlearning network algorithm. The simulation experiment is carried out by using SUMO traffic simulation software. The performance of the proposed method is verified based on the traffic data from Pingxiang City, Jiangxi Province and it shows that the proposed method can effectively alleviate the aggregate delay of the early and late traffic peaks compared with fixed time.