The invention relates to the technical field of intelligent traffic, in particular to a traffic flow prediction method based on Transform and a graph neural network, and the method comprises the steps: obtaining flow time sequence data from different traffic sensor nodes; extracting a time sequence characteristic of each node by using a Transform encoder, and generating an embedded vector of each traffic node; based on the embedded vector between the nodes, calculating the similarity between the nodes by using cosine similarity, and constructing a dynamic graph structure to reflect the spatial relationship between the nodes; neighbor information of the nodes is aggregated by using a graph attention mechanism in the graph neural network, and learning of spatial features is enhanced; according to the method, the time sequence features and the space features are fused, the future traffic flow is predicted through the full connection layer, the complex space-time relation in the traffic data can be effectively captured, and the method adapts to the change of the real road structure through the dynamic graph structure, so that the accuracy and robustness of traffic flow prediction are improved.
本发明涉及智能交通技术领域,具体为一种基于Transformer和图神经网络的交通流量预测方法,包括:获取来自不同交通传感器节点的流量时间序列数据;利用Transformer编码器提取每个节点的时序特征,生成每个交通节点的嵌入向量;基于节点间的嵌入向量,使用余弦相似度计算节点之间的相似性,并构建动态图结构以反映节点间的空间关系;运用图神经网络中的图注意力机制对节点的邻居信息进行聚合,强化空间特征的学习;融合时序特征和空间特征,并通过全连接层对未来的交通流量进行预测,能够有效捕捉交通数据中的复杂时空关系,并通过动态图结构适应现实道路道路结构的变化,从而提升交通流量预测的准确性和鲁棒性。
Transform and graph neural network-based traffic flow prediction method
一种基于Transformer和图神经网络的交通流量预测方法
2025-04-18
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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