The invention discloses a traffic flow prediction method based on time-space synchronization GraphSAGE. The method comprises the following steps: generating traffic flow data of a time sequence; according to the spatial adjacency matrix and the Spearman correlation coefficient matrix, firstly constructing an inclusive spatial adjacency matrix, and then designing an inclusive space-time synchronization diagram; according to the method, a space-time synchronization GraphSAGE model is constructed, traffic characteristics of space-time neighbors from 1 order to K order are aggregated through an attention mechanism for each traffic node according to a GraphSAGE thought, and the traffic characteristics are spliced with the characteristics of the traffic node, so that space-time dependence of traffic flow data is synchronously learned in an inductive manner. According to the method, space-time synchronous modeling of the traffic flow data is realized by designing the inclusive space-time synchronous graph, so that the model achieves accurate prediction precision, and meanwhile, the limitation of full-graph training and direct-push learning in the conventional traffic flow prediction method is solved based on the space-time characteristics of the GraphSAGE inductive aggregation traffic nodes.
本发明公开了一种基于时空同步GraphSAGE的交通流量预测方法,包括:生成时间序列的交通流数据;根据空间邻接矩阵和斯皮尔曼相关系数矩阵,先构建包容式空间邻接矩阵,进而设计包容式时空同步图;构建时空同步GraphSAGE模型,该模型依照GraphSAGE思想,对每个交通节点,通过注意力机制聚合其1到K阶时空邻居的交通特征,并与该交通节点的本身特征进行拼接,以此归纳式同步学习交通流数据的时空依赖。本发明通过设计包容式时空同步图,实现交通流数据的时空同步建模,使模型达到精确的预测精度,同时基于GraphSAGE归纳式聚合交通节点的时空特征,解决了以往交通流量预测方法中全图训练及直推式学习的局限性。
Traffic flow prediction method based on space-time synchronization GraphSAGE
一种基于时空同步GraphSAGE的交通流量预测方法
2024-11-26
Patent
Electronic Resource
Chinese
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