Traffic Flow Prediction Based on Dynamic Graph Multi Temporal Perspectives Attention Network

Traffic flow prediction is an important technology in intelligent transportation systems (ITS) . Accurate traffic prediction can reduce congestion and improve traffic efficiency. However, traffic flow data contains complex temporal relationships, and capturing dynamic traffic spatial relationships is a challenge. In order to improve the prediction accuracy, a dynamic graph multi temporal perspectives attention network (DGMAN) is proposed,based on the spatiotemporal data of traffic flow. The model uses a dynamic graph learning module (DGLM) to extract the dynamic relationship information between traffic nodes in traffic data by establishing a dynamic graph. In complex temporal data, the multi temporal perspectives attention mechanism (MtpA) captures the temporal dependence of traffic flow and mines potential temporal relationships. Finally, the proposed model is tested on 4 real-world datasets. Compared with the baseline models, DGMAN achieves the best performance in the mean absolute error (MAE) , root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metrics.