The invention discloses a traffic flow prediction method based on a dynamic multi-view space-time fusion graph convolutional network, and the method comprises the following steps: collecting road network information, obtaining original data, and carrying out the normalization of the original data; a dynamic multi-view space-time fusion graph convolutional network is constructed, and the network structure is composed of a time decomposition layer, a time gating convolution (TGC) module and an adaptive multi-graph convolution (AMC) module; the time decomposition layer integrates various types of features into dynamic graph embedding, so that the defects of a traditional adjacent matrix in the aspect of simulating a real road network are relieved; the TGC module filters irrelevant information in a plurality of periods through a gating mechanism, so that the model can learn a long-term time mode in traffic data; and the AMC module provides diversified features for the model by using multi-graph embedding, so that the traffic flow prediction precision is further improved.
本发明公开一种基于动态多视图时空融合图卷积网络的交通流预测方法,包括以下步骤:采集路网信息得到原始数据,并归一化;构造一种动态多视图时空融合图卷积网络,网络结构由时间分解层、时间门控卷积(TGC)模块和自适应多图卷积(AMC)模块组成;时间分解层将多种类型的特征集成到动态图嵌入中,从而缓解传统邻接矩阵在模拟真实道路网络方面的不足;TGC模块通过门控机制过滤多个周期中的无关信息,使模型能够学习交通数据中的长期时间模式;AMC模块利用多图嵌入为模型提供多样化特征,实现交通流预测精度的进一步提高。
Traffic flow prediction method based on dynamic multi-view space-time fusion graph convolutional network
基于动态多视图时空融合图卷积网络的交通流预测方法
2025-01-03
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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