MGSTCN: A Multi-Graph Spatio-Temporal Convolutional Network for Metro Passenger Flow Prediction

Abstract

Short-term passenger flow forecasting is significantly important in the urban rail transit system. However, there are many factors that affect passenger flows, and traditional statistical prediction methods are unable to model these complex factors comprehensively. Emerging deep learning models have become effective methods to overcome this problem. In this study, we propose a new deep learning model MGSTCN (Multi-Graph Spatio-Temporal Convolutional Network) to predict short-term passenger flows for all stations in an urban subway network. Specifically, MGSTCN is composed of three modules with the same structure, which models the temporal dependencies of passenger flows in three different time scales, i.e., recent, daily and weekly periods respectively. Each module contains three main components: a multi-graph convolutional layer, a feature fusion layer, and a recurrent neural network. In addition, we construct three topological graphs based on the subway network, station attributes, and passenger flow trends, to extract spatial features from different perspectives through graph convolutional networks. Besides, we also employ some external factors such as weather conditions and holidays to enhance the forecasting accuracy of our proposed model. We conduct experiments based on two real-world passenger flow datasets in Shanghai and Hangzhou, respectively. The results show that our proposed model has superior performance compared to the baselines.