The invention belongs to the technical field of urban intelligent traffic signal control, and particularly relates to a multi-intersection traffic signal control method fused with traffic flow prediction, which comprises the following steps of: modeling a road network structure by utilizing a heterogeneous graph representation method, determining signal lamp nodes, intersection nodes and lane nodes of a heterogeneous graph in a road network, and calculating a traffic flow prediction model; and calculating a spatial association relationship between the nodes. According to the method, the traffic flow of the traffic network is predicted by using the long-short-term memory neural network, the time sequence and space structure information of a plurality of time steps is captured from historical, current and predicted traffic flows through the application of the graph neural network, and a reward function for sensing the space-time information is provided. The function uses betweenness centrality to evaluate the spatial importance of each intersection, and introduces predicted traffic flow information as a parameter composition of the function, so that the ability of an intelligent agent to perceive spatio-temporal information can be effectively improved.
本发明属于城市智能交通信号控制技术领域,尤其是涉及一种融合交通流预测的多交叉口交通信号控制方法,包括如下步骤:利用异构图表示法建模路网结构,确定路网中异构图的信号灯节点、交叉口节点以及车道节点,及节点间的空间关联关系。本发明利用长短期记忆神经网络对交通网络的车流量进行预测,通过应用图神经网络,它从历史、当前和预测的交通流中捕获多个时间步的时序和空间结构信息,提出了一种感知时空信息的奖励函数,该函数利用介数中心性来评估各个交叉口的空间重要性,并引入预测的车流量信息作为该函数的参数构成,能有效提高智能体感知时空信息的能力。
Multi-intersection traffic signal control method fusing traffic flow prediction
一种融合交通流预测的多交叉口交通信号控制方法
2025-05-30
Patent
Electronic Resource
Chinese
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