The invention relates to the technical field of traffic control, in particular to a single-point traffic signal control method based on intersection holographic data, and the method comprises the steps: constructing a deep reinforcement learning model comprising a plurality of agents, then carrying out the neural network training through employing an MARDDPG algorithm, and finally carrying out the signal control through employing a trained neural network. Different from a traditional traffic detection technology, the intersection holographic data can be fully utilized to completely express traffic demands, meanwhile, deep reinforcement learning of the MARDDPG algorithm is adopted, each agent independently controls each phase, collaborative optimization is carried out between single agents, intersection control efficiency optimization is easier to achieve, and further, the intersection control efficiency is improved. As the intelligent agent independently controls each phase, the state space of each intelligent agent is small, the result convergence is fast, and the traffic signal can be effectively and more accurately controlled.

    本发明涉及交通控制技术领域,具体涉及一种基于交叉口全息数据的单点交通信号控制方法,构建包含多个智能体的深度强化学习模型,再使用MARDDPG算法进行神经网络训练,最后使用训练完成的神经网络进行信号控制,区别于传统的交通检测技术,本发明充分利用交叉口全息数据能够将交通需求完全表达出来,同时采用MARDDPG算法的深度强化学习,每个智能体单独控制每个相位,单个智能体之间进行协同优化,更容易达到交叉口控制效率最优,进一步的,由于智能体单独控制每个相位,每个智能体的状态空间较小,结果收敛较快,能有效以及更精确地控制交通信号。


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    Title :

    Single-point traffic signal control method based on intersection holographic data


    Additional title:

    一种基于交叉口全息数据的单点交通信号控制方法


    Contributors:
    WANG TAO (author) / ZHAO XIAOYIN (author) / CHENG RUI (author) / XU QI (author)

    Publication date :

    2023-02-03


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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