RA-TSC: Learning Adaptive Traffic Signal Control Strategy via Deep Reinforcement Learning

Abstract

To reduce the traffic congestion, ensuring intersection control strategy efficiency is a priority in urban transportation system. Reinforcement Learning (RL)-based method provides a new possibility for solving this complex problem. In this paper, a novel region adaptive traffic signal control (RA-TSC) strategy is proposed. As an intelligent agent, the traffic signal light can learn the optimal control policy via deep reinforcement learning algorithm, namely Double Dueling Deep Q Network (3DQN). Different from the previous researches, the proposed method takes traffic characteristics into account, and two approaches are proposed to improve the performance of intersection control: trust region state (TRS) and standardized reward (SR). Besides, prioritized experience replay is adopted to guarantee algorithm stability and optimize data exploitation. Simulation results show that the proposed RA-TSC algorithm can reduce vehicle waiting length by 20% and 15.1% respectively when compared with fixed time and original 3DQN control algorithms.