The invention discloses a deep learning traffic flow prediction method based on a time perception hypergraph, and the method comprises the steps: constructing a mixed hypergraph through traffic flow data, so as to describe the space structure information of a traffic network, including the position information of nodes and the high-order relation between the nodes; and constructing a time-aware hypergraph to represent the dynamic periodic characteristics of the traffic flow data. Secondly, in combination with space structure information and dynamic periodic characteristics of a traffic road network, a time module based on a time encoder and a time attention network is used for extracting and mining a time dependency relationship in traffic flow data; a spatial module based on a spectral domain spatial hypergraph encoder and a hypergraph attention network is used for deeply mining a spatial dependency relationship in traffic flow data, and a multi-layer perceptron feature fusion device is used for fusing various time and spatial features into a space-time dependency relationship. And finally, mapping the space-time dependency relationship into a future traffic flow predicted value by using a predictor based on a full connection layer.
本发明公开了基于时间感知超图的深度学习交通流量预测方法,首先,利用交通流量数据构建混合超图以描述交通路网的空间结构信息,包括节点的位置信息和节点间的高阶关系,并构建时间感知超图以表示交通流量数据的动态周期特性。其次,结合交通路网的空间结构信息和动态周期特性,利用基于时间编码器和时间注意力网络的时间模块提取挖掘交通流量数据中的时间依赖关系,利用基于谱域空间超图编码器和超图注意力网络的空间模块深入挖掘交通流量数据中的空间依赖关系,并利用多层感知机特征融合器将各种时间和空间特征融合为时空依赖关系。最后,利用基于全连接层的预测器将时空依赖关系映射为未来的交通流量预测值。
Deep learning traffic flow prediction method based on time perception hypergraph
一种基于时间感知超图的深度学习交通流量预测方法
2024-11-15
Patent
Electronic Resource
Chinese
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