The invention discloses a traffic flow prediction method and system based on a bidirectional spatio-temporal expansion graph convolutional network. The method comprises the following steps: acquiring historical traffic flow data of each node in a road network in real time and taking the historical traffic flow data as initial traffic flow features; performing feature fusion and causal convolution on the initial traffic flow features in sequence to obtain traffic flow data with time sequence features; calculating a two-way normalized adjacent matrix corresponding to the road network, and performing extended graph convolution operation with an ARMA filter on the traffic flow data and the normalized adjacent matrix in each direction to obtain each layer of information of extended graph convolution in each direction; and aggregating each layer of information of the expansion graph convolution in the same direction to obtain a layer aggregation feature of the expansion graph convolution in each direction, and then obtaining final output through attention aggregation. According to the method, the prediction precision is improved, and the model parameter quantity and the training time are reduced to meet the requirement of real-time traffic flow prediction.
本发明公开了一种基于双向时空扩展图卷积网络的交通流预测方法及系统,方法包括:实时获取路网中每个节点的历史交通流数据并作为初始交通流特征;将初始交通流特征依次经过特征融合和因果卷积,得到带有时序特征的交通流数据;计算路网对应的双向归一化邻接矩阵,分别将交通流数据与每个方向的归一化邻接矩阵进行带有ARMA滤波器的扩展图卷积操作,得到每个方向的扩展图卷积的每层信息;将同一方向的扩展图卷积的每层信息聚合,得到每个方向的扩展图卷积的层聚合特征后通过注意力聚合得到最终的输出。本发明提高预测精度的同时减少模型参数量和训练时间以满足实时交通流预测的需求。
Traffic flow prediction method and system based on bidirectional spatio-temporal expansion graph convolutional network
一种基于双向时空扩展图卷积网络的交通流预测方法及系统
2025-04-15
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
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