Subway passenger flow prediction based on GAT-LSTM-attention considering the graph construction of passenger flow difference

The accurate prediction of subway passenger flow is of great significance for the intelligent management of urban rail transit systems. However, traditional static network prediction models struggle to capture the dynamic correlations of subway passenger flow, and the graph structure generated by the DTW algorithm may overlook the differences between passenger flows. Therefore, this paper proposes a GAT-LSTM-Attention prediction model based on a graph structure constructed from the differences in passenger flow. Firstly, a graph structure is constructed based on the differences in peak and valley values between stations, so that the prediction of a station is not affected by stations that exhibit significant differences. Secondly, the periodic characteristics and weather features that may affect the passenger flow prediction are extracted and fused. Finally, the prediction is carried out by the GAT-LSTM-Attention. Through the dual attention mechanism of GAT and Attention, the model is capable of effectively capturing spatial features and key information. The experimental results show that the proposed model has lower MAE and RMSE error values compared with the baseline models. In the 30-minute and 60-minute prediction of outbound and inbound passenger flow, the proposed model achieves the higher R2 values of 0.99, 0.99, 0.98, and 0.97. And the proposed graph structure based on passenger flow difference has lower MAE and RMSE error values than conventional methods for constructing graphical structures. This study provides a valuable method for passenger flow prediction in urban rail transit systems.