Urban Road Traffic Prediction With Dynamic Spatial-Temporal Graph Convolutional Network Based on Attention Mechanism

As intelligent transportation systems continue to develop, the real-time and accurate traffic prediction in urban road networks becomes increasingly critical. Urban road network topology frequently changes due to traffic light control. To capture the dynamic spatial correlations among urban roads, this article introduces a traffic forecasting method utilizing a dynamic spatial-temporal graph convolutional network (D-TGCN) model integrated with an attention mechanism. The method incorporates the traffic BERT model to enhance the static adjacency matrix in traditional graph convolutional neural networks (CNNs) by leveraging the attention mechanism, thereby capturing the implicit correlation between the dynamic variations in the urban road network and traffic flow. First, the road network is transformed into dynamic graph sequences, which traffic BERT uses to generate the final dynamic correlation matrix. Subsequently, the graph convolutional network (GCN) is employed alongside the dynamic correlation matrix to capture dynamic spatial dependencies, while temporal dependencies are modeled using a gated recurrent unit (GRU). LOS-loop and SZ-taxi are the two real-world traffic datasets that were used to test and validate the enhanced model. Results from the experiments show that the D-TGCN model outperformed the temporal graph convolutional network (T-GCN) model by 11.08%, 12.23%, 13.05%, and 13.71% in prediction tasks spanning 15, 20, 30, 45, and 60 min. These results show that the D-TGCN model gives considerable benefits for long-term forecasting and delivers improved prediction accuracy.