Multi-weighted Graphs Learning for Passenger Count Prediction on Railway Network

In this study, a method of multi-weighted graphs learning for passenger count prediction in railway networks, is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by various factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task because of the complexity of the railway networks. To achieve high learning performance of the models and discover the correlation between the models and features, we proposed various heterogenerous weighted graphs for the passenger count prediction. Six types of weight graphs, that is, connection graph, distance graph, correlation graph, and their fused weight graphs were proposed to fully construct the spatial and geometrical features within the entire railway network. Two representative types of graph neural networks, that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RSME), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Finally, the importance and effectiveness of the models with corresponding weight graphs were also investigated and explained. It also provides the interpretability of the traffic prediction tasks on the railway network.