The invention provides a traffic flow prediction method based on multivariate spatial-temporal feature extraction, and the method comprises the steps: employing a multivariate spatial-temporal feature extraction model, and employing a Res2Net module improved through gating convolution in a time dimension, so as to capture a change mode of a traffic flow along with a time scale; the method comprises the following steps of: capturing global spatial correlation, geographic neighborhood spatial correlation and semantic similarity spatial correlation among nodes in an urban road network by using a spatial attention mechanism and a double-view Geom-GCN from global and local perspectives in a spatial dimension; on the time-space interaction dimension, a dynamic space-time synchronous aggregation module constructed by using space-time attention is used to capture a dynamic interaction relationship between nodes or regions across time slices so as to predict the future road traffic flow.
本发明提供一种基于多元时空特征提取的交通流量预测方法,采用多元时空特征提取模型,在时间维度上,利用门控卷积改进的Res2Net模块,以捕获交通流随时间尺度变化模式;在空间维度上,从全局和局部视角出发,分别使用空间注意力机制和双视图Geom‑GCN,捕获城市路网中节点之间的全局空间相关性、地理邻域空间相关性和语义相似空间相关性;在时间‑空间交互维度上,使用时空注意力构建的动态时空同步聚合模块,捕获跨时间片下节点或区域之间的动态交互关系,以预测未来道路交通流。
Traffic flow prediction method based on multivariate spatial-temporal feature extraction
一种基于多元时空特征提取的交通流量预测方法
22.04.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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