MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow's daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods.

Automated summary

The passenger flow prediction of the public metro system is crucial for traffic management, metro planning, and emergency safety measures. This study proposes a multi-time multi-graph neural network model named MTMGNN, which combines a long-term Gated Convolutional Neural Network (Gated CNN) and a multi-graph module to extract temporal and spatial features simultaneously. Experimental results on real-world datasets show that MTMGNN outperforms state-of-the-art methods.