Attention-based spatio-temporal graph convolutional network for dynamic traffic flow prediction
Most existing methods ignored the impact of spatio-temporal coupling correlation,spatio-temporal variability,and external features on the accuracy of prediction results.In response to the above problems,this paper proposed a spatio-temporal attention graph convolution network model(attention-based spatio-temporal graph convolutional network,ATST-GCN)for dynamic traffic flow prediction.An attention-based bidirectional GRU structure was proposed to extract temporal correlation from dynamic spatial sequences.A multi-layer GAT(graph attention network,GAT)convolution module with residual connection was constructed to deeply extract the dynamic spatial correlation.Time-varying features and constant features were integrated to make full use of the joint effect of external static and dynamic features.The PeMS dataset was used for verification of the accuracy of traffic flow prediction using PeMs dataset.The experiment results showed that the method proposed in this paper could effectively improve the accuracy of traffic flow prediction and was better than most existing advanced methods.On the PeMS08 and PeMS03 datasets,the method of this sdudy improved 13.44%and 10.96%relative to the STSGCN model,21.41%and 21.32%relative to the T-GCN model,8.04%and 6.55%relative to the STGCN model,3.23%and 2.80%relative to the DMSTGCN model,2.29%and 2.00%respectively relative to the Trendformer model.
万方数据
维普
