The invention provides a traffic flow prediction method based on a space-time causal attention network. The method comprises the following steps: coding all road nodes through node embedding; self-adaptively learning the relationship between road nodes according to the road traffic conditions at different moments; the relation between the nodes is combined with a graph attention network GAT through a multi-head time attention module to form a node adaptive learning graph attention network AGAT; the space-time causal attention network STCAN reconstructs the node adaptive learning graph attention network AGAT into a gate circulation unit GRU; the space-time causal attention network STCAN captures the space-time causal relationship of the traffic data in a fine-grained manner by using a two-way gate circulation unit GRU; a residual module is introduced to reduce network degradation caused by the deep network; the space-time causal attention network STCAN is combined with the multi-head time attention module to process time sequence data; and a multi-step flow prediction task is realized by performing linear change on the two-layer full-connection neural network pair.
本发明提供一种基于时空因果注意力网络的交通流预测方法,包括以下步骤:通过节点嵌入对所有道路节点进行编码;根据不同时刻的道路交通状况自适应学习道路节点之间的关系;通过多头时间注意模块将节点之间的关系与图注意网络GAT结合,形成节点自适应学习图注意网络AGAT;时空因果注意力网络STCAN将节点自适应学习图注意网络AGAT重构为门通循环单元GRU;时空因果注意力网络STCAN利用双向门通循环单元GRU,以细粒度的方式捕捉交通数据的时空因果关系;引入残差模块,降低由深度网络带来的网络退化;时空因果注意力网络STCAN通过与多头时间注意模块结合处理时间序列数据;通过对两层全连接神经网络对进行线性变化来实现多步流量预测任务。
Traffic flow prediction method based on space-time causal attention network
一种基于时空因果注意力网络的交通流预测方法
2024-12-10
Patent
Electronic Resource
Chinese
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