The invention provides a traffic flow prediction method and system based on a space-time diagram convolutional neural network, and the method comprises the steps: obtaining the related information of an expressway, and carrying out the corresponding preprocessing; wherein the related information comprises road charging information, road attribute information and road traffic information; based on the road charging information and the road attribute information, coding the road charging information into a graph adjacency matrix by using a pre-constructed graph self-learning module, and obtaining an optimal graph adjacency matrix; and based on the road charging information and the road traffic information as node information, combining the optimal graph adjacency matrix, and utilizing a pre-trained time-space graph convolutional neural network model based on a time attention mechanism to obtain a traffic flow prediction result of the expressway.
本发明提供了一种基于时空图卷积神经网络的交通流预测方法及系统,包括:获取高速公路的相关信息,并进行相应预处理;其中,所述相关信息包括道路收费信息、道路属性信息以及道路交通信息;基于所述道路收费信息及道路属性信息,利用预先构建的图自学习模块将道路收费信息编码至图邻接矩阵中,并获得最优的图邻接矩阵;基于作为节点信息的所述道路收费信息及道路交通信息,并结合所述最优图邻接矩阵,利用预先训练的基于时间注意力机制的时空图卷积神经网络模型,获得高速公路的交通流预测结果。
Traffic flow prediction method and system based on space-time diagram convolutional neural network
基于时空图卷积神经网络的交通流预测方法及系统
25.07.2023
Patent
Elektronische Ressource
Chinesisch
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