Considering the intricate traffic flow dynamics between the mainline and entrance ramp areas of highways, a ramp control model based on deep reinforcement learning (DRL) is proposed. Leveraging the characteristics of traffic flow, this study builds upon existing reinforcement learning (RL) ramp control models and optimizes the design of the state space, action space, and reward function. Utilizing the PyCharm platform, SUMO simulation software is adopted for secondary development. Simulation experiments are conducted in various scenarios involving different congestion durations and demand distributions. Under identical road network conditions, four distinct control methods are devised: the no-control (NC) scenario, the ALINEA ramp control method, the RL control method, and the proposed DRL method. The experimental outcomes indicate that, compared to the NC method, all three alternative methods substantially reduce the average travel time. The proposed DRL method exhibits an average travel time reduction of 12.77% compared to the ALINEA method and a 5.22% reduction compared to the RL method. Furthermore, the proposed DRL method achieves a reduction of approximately 39.01% in the ramp queue length compared to the ALINEA method.


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    Titel :

    A Highway Entrance Ramp Control Method Based on Deep Reinforcement Learning


    Beteiligte:
    Wu, Lin (Autor:in) / Li, Zhenlong (Autor:in) / Diao, Mengxiao (Autor:in) / Dong, Xiaobo (Autor:in) / Ou, Jushang (Autor:in)


    Erscheinungsdatum :

    14.12.2023


    Format / Umfang :

    320872 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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