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.


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    Title :

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


    Contributors:
    Du, Yu (author) / ShangGuan, Wei (author) / Rong, Dingchao (author) / Chai, Linguo (author)


    Publication date :

    2019-10-01


    Size :

    1117996 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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