The invention belongs to the technical field of traffic transportation, and particularly relates to a traffic flow prediction method based on a dynamic sparse graph convolution GRU, which integrates a space-time attention fusion scheme of a gating mechanism, adds a dynamic sparse graph convolution gating cycle unit DSGCN-GRU in a model, and is adaptive to a dynamic sparse graph convolution embedded gating cycle network. Dynamic spatial structure information diffusion is simulated, heterogeneous features and local features of spatial data are effectively captured, the irregularity and dynamic variability represented by spatial information are further reflected, the understanding of the model on local and global spatial-temporal features is improved through the spatial-temporal attention of a gating mechanism, and the spatial-temporal attention of the gating mechanism is optimized. Unified description of a multi-scale and long-distance space-time mode is realized, and the strain of the model to long-term traffic flow prediction and sudden traffic events is enhanced. Compared with an existing traffic flow prediction method, the traffic flow prediction accuracy can be effectively improved.
本发明属于交通运输技术领域,具体涉及一种基于动态稀疏图卷积GRU的交通流预测方法,集成了门控机制的时空注意力融合方案,模型中增加了动态稀疏图卷积门控循环单元DSGCN‑GRU,自适应动态稀疏图卷积嵌入门控循环网络,模拟动态空间结构信息扩散,有效地捕捉空间数据的异构特征与局部特性,进一步反映空间信息所表现出的非规则性和动态变化性,通过门控机制的时空注意力,提升模型对局部与全局时空特征的理解力,实现多尺度、远距离时空模式的统一刻画,强化模型对长期交通流量预测及突发交通事件的应变。与现有的交通流预测方法相比,本发明能有效地提高交通流预测的准确性。
Traffic flow prediction method based on dynamic sparse graph convolution GRU
一种基于动态稀疏图卷积GRU的交通流预测方法
2025-03-25
Patent
Electronic Resource
Chinese
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