DSP-ST: Dynamic Structural Prior Spatiotemporal Graph Attention Networks for Traffic Speed Prediction

Accurate traffic forecasting is essential for intelligent transportation system management and control. Due to the highly complex spatiotemporal (ST) correlation of real-world road networks, dynamic and long-term traffic prediction presents many challenges. We propose a traffic speed prediction model based on dynamic structural prior (DSP) ST graph attention networks. We provide a structural prior graph, namely, dual graph convolution, which combines spatial and contextual subgraphs to enable the discovery of the non-Euclidean spatial correlation and potential contextual similarity of road networks. Moreover, to dynamically extract the ST correlation, this article employs a multihead self-attention temporal convolution module to capture the temporal correlation and a graph attention convolution module to extract the spatial correlation. The prediction output is generated by stacking multiple ST blocks. Experimental results on two real-world traffic datasets demonstrate that DSP-ST outperforms existing mainstream baselines, which can provide references for traffic management departments.