The application of modern technologies makes it possible for a transportation system to collect real-time data of some specific traffic scenes, helping traffic control center to improve the traffic efficiency. Based on such consideration, we introduce a variant deep reinforcement learning agent that might take advantage of the real-time GPS data and learn how to control the traffic lights in an isolated intersection. We combine the recurrent neural network (RNN) with Deep Q-Network, namely DRQN and compare its performance with standard Deep Q-Network (DQN) in partially observed traffic situations. The agent is trained by using Q-learning with experience replay in traffic simulator SUMO, so as to generate traffic signal control policy. Based on the experiments, both DQN and DRQN method are able to adjust its traffic signal timing policy to specific traffic environment and achieve lower average vehicle delay than fixed time control. In addition, the recurrent Q-learning method gets better simulation result than standard Q-learning method in the environment of different probe vehicle proportion.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Traffic Signal Control with Deep Recurrent Q-learning


    Beteiligte:
    Zeng, Jinghong (Autor:in) / Hu, Jianming (Autor:in) / Zhang, Yi (Autor:in)


    Erscheinungsdatum :

    2018-06-01


    Format / Umfang :

    715339 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Recurrent Q Networks for Urban Traffic Signal Control

    Zhang, Xiongfei / Mo, Huijuan / Ma, Hongzhuang et al. | ASCE | 2022


    Deep Learning vs. Discrete Reinforcement Learning for Adaptive Traffic Signal Control

    Shabestary, Soheil Mohamad Alizadeh / Abdulhai, Baher | IEEE | 2018


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

    Du, Yu / ShangGuan, Wei / Rong, Dingchao et al. | IEEE | 2019


    Self-adaptive traffic signal control method based on graph deep reinforcement learning

    ZHAO ZHONGNAN / WANG KUN / SONG XIN | Europäisches Patentamt | 2022

    Freier Zugriff

    Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

    Duowei Li / Jianping Wu / Ming Xu et al. | DOAJ | 2020

    Freier Zugriff