MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction
Image credit: UnsplashAbstract
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.
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Yongkang Li
PhD Student
I am currently a PhD student in IR LAB, the University of Amsterdam, work with Porf. Evangelos Kanoulas. Before that, I got my master degree at Southern University of Science and Technology, Department of Computer Science and Engineering, SUSTech-UTokyo Joint Research Center on Super Smart City Lab, where I am supervised by Prof. Xuan Song in SUSTech and Prof. Zipei Fan at the University of Tokyo. What’s more, I received a B.E. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications in 2020.