Arterial Traffic Optimization Algorithm Based on Deep Reinforcement Learning and Green Wave Coordination Control in Complex Lane Queuing Conditions

With the development of transportation, the traditional traffic signal systems being unable to provide dynamic and flexible timing schemes for urban arterial road traffic in complex lane queuing conditions. In the control of arterial traffic, to solve the problem that vehicles queuing in turning lanes of branch road and then congesting the arterial road, this paper proposed an arterial traffic optimization algorithm based on deep reinforcement learning (DRL) and green wave coordination control in complex lane queuing conditions. The proposed algorithm provides a detailed division of the arterial roads and analyzed the mutual influence between vehicles inside the roads, combines DRL algorithm with the MAXBAND algorithm to optimize the signal period, phase sequence and green signal ratio of arterial roads, creates a new reward function for Deep Q Network (DQN) algorithm for multi-agent coordination. The algorithm was validated in SUMO simulation environment. The simulation results prove that the algorithm can flexibly perform signal timing and is more effective than traditional algorithms.