Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction

One of the challenging topics in Intelligent Transportation Systems (ITSs) is the metro passenger flow prediction. It has great practical significance for the daily crowd management and vehicle scheduling of metro passenger flow. Recently, Graph Convolutional Networks (GCN) represent a station of metros by aggregating information of stations directly and indirectly connected with the station, and improve the effectiveness of predicting metro passenger flow. Despite its effectiveness, the neighborhood aggregation scheme also brings two limitations in predicting metro passenger flow. First, it limits to predict accurately the peak of passenger flow with large data fluctuation. Second, it enlarges the impact of noisy data and makes results vulnerable to noisy data of metro passenger flow. To solve these problems, we propose regularized spatial-temporal graph convolutional networks for metro passenger flow prediction (PMR-GCN). More specifically, we first propose a novel personalized enhanced GCN (P-GCN), which defines a trainable diagonal matrix to adaptively learn to control the impact of the neighborhood aggregation scheme for predicting the peak of passenger flow. Then, we introduce the multi-head self-attention mechanism to capture richer spatial-temporal features of passenger flow. In addition, we utilize a Fourier Transform module and a dual structure based on KL-divergence regularization to improve the robustness of the proposed model. Extensive experiments on Shanghai, Chongqing, and Hangzhou datasets demonstrate the superiority of our model over the state-of-the-art baseline methods. The code of PMR-GCN is available at https://github.com/cgao-comp/PMC-GCN.