The invention discloses a multi-intersection traffic signal control method based on heuristic deep Q learning, and mainly solves problems of correlation of training data, incapability of rapid convergence of a traffic light control strategy and low control efficiency in an existing method. The method comprises steps that the urban traffic network information is read, a vehicle traffic state set ofeach intersection is established, and the read urban traffic network information is converted into an adjacent matrix to be stored; a state set, an action set and an action reward value of each intersection are acquired from a vehicle traffic state set of each intersection; according to the state set, the action set, the action reward value and the adjacency matrix, a heuristic deep Q network method is used for continuously executing actions according to the state of each intersection to obtain rewards and then to the next state, and urban road network traffic lights are controlled. The method can improve control efficiency of intersection traffic signal lamps, improves performance of the multi-intersection traffic signal controller, can be used for urban traffic management, and reduces the urban traffic congestion.
本发明公开了一种基于启发式深度Q学习的多路口交通信号控制方法,主要解决现有方法中的训练数据有相关性、交通灯控制策略不能快速收敛和控制效率低的问题。其方案是:读取城市交通路网信息,建立各个路口的车辆交通状态集合,并将读取的城市交通路网信息转换为邻接矩阵进行存储;从各个路口车辆交通状态集合中,获取各个路口状态集、动作集和动作奖励值;根据状态集、动作集、动作奖励值和邻接矩阵,利用启发式深度Q网络方法根据每个路口的状态不断地执行动作获取奖励再到下一状态,实现对城市路网交通灯进行控制。本发明能提高对路口交通信号灯的控制效率,改善了多路口交通信号控制器的性能,可用于城市交通管理,减少城市交通拥堵。
Traffic light control method based on heuristic deep Q network
基于启发式深度Q网络的交通灯控制方法
2020-09-22
Patent
Electronic Resource
Chinese
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