An Information Fusion Approach to Intelligent Traffic Signal Control Using the Joint Methods of Multiagent Reinforcement Learning and Artificial Intelligence of Things

With the development of communication technology and artificial intelligence of things (AIoT), transportation systems have become much smarter than ever before. However, the volume of vehicles and traffic flows have rapidly increased. Optimizing and improving urban traffic signal control is a potential way to relieve traffic congestion. In general, traffic signal control is a sequential decision process that conforms to the characteristics of reinforcement learning, in which an agent constantly interacts with its environment, thus providing strategy for optimizing behavior in accordance with feedback in response. In this paper, we propose multiagent reinforcement learning for traffic signals (MARIATS) to support the control and deployment of traffic signals. First, information on traffic flows and multiple intersections is formalized as input environments for performing reinforcement learning. Second, we design a new reward function to continuously select the most appropriate strategy as control during multiagent learning to track actions for traffic signals. Finally, we use a supporting tool, Simulation of Urban MObility (SUMO), to simulate the proposed traffic signal control process and compare it with other methods. The experimental results show that our proposed MARIATS method is superior to the baselines. In particular, our method can reduce vehicle delay.

Automated summary

With the advancement of communication technology and AIoT, transportation systems have become smarter, but the increase in vehicle volume has led to traffic congestion. Optimizing urban traffic signal control is an effective way to alleviate this issue. The proposed multiagent reinforcement learning for traffic signals has shown to reduce vehicle delay, outperforming other methods.