本发明公开了一种基于强化学习的城市交通堵塞调度方法,通过图像传感器和电感传感器获取城市道路交叉口的车辆数量信息、车辆排队信息和交通灯状态的实时数据;再利用机器学习算法,根据车辆数量信息、车辆排队信息和交通灯状态的实时数据,结合从图像信息与储备结构化数据获取的路段限制与车道信息的交叉口先验知识,形成交叉口路况状态数据作为调度模型训练数据;调度模型根据环境反馈的交叉口各车道的通行效果和奖励函数计算奖励信号,从而训练调度模型;利用强化学习算法,基于交叉口路况状态数据与交叉口通行安全准则训练调度模型;以交叉口路况状态数据作为输入,通过完成训练后的调度模型输出交通灯状态指令及相应交通灯控制信号。

    The invention discloses an urban traffic jam scheduling method based on reinforcement learning. The method comprises the following steps: acquiring real-time data of vehicle quantity information, vehicle queuing information and traffic light states of an urban road intersection through an image sensor and an inductive sensor; using a machine learning algorithm to form intersection road condition state data as scheduling model training data according to real-time data of vehicle number information, vehicle queuing information and traffic light states and in combination with intersection prior knowledge of road section limitation and lane information obtained from image information and reserve structured data; the scheduling model calculates a reward signal according to the passing effect of each lane of the intersection fed back by the environment and a reward function, so as to train the scheduling model; using a reinforcement learning algorithm to train a scheduling model based on the intersection road condition state data and the intersection traffic safety criterion; and taking intersection road condition state data as input, and outputting a traffic light state instruction and a corresponding traffic light control signal through the trained scheduling model.


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    Titel :

    基于强化学习的城市交通堵塞调度方法


    Erscheinungsdatum :

    2023-07-04


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung