Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting

Subway passenger flow forecasting, an essential component of intelligent transportation system, is critical for traffic management, public safety, urban planning. However, it is very challenging due to the high nonlinearities and complex dynamic spatio-temporal dependencies of passenger flows. In this paper, we model the subway system as a directed weighted graph and propose a novel spatio-temporal deep learning framework, Multi-STGCnet, for forecasting short-term subway passenger flow at a station level. Specifically, Multi-STGCnet is mainly composed of two components, temporal component and spatial component. (1) The temporal component employs three long short-term memory network (LSTM)-based modules to capture three temporal properties of the target station, which are the interval closeness, daily periodicity, weekly trend. (2) The spatial component designs three spatial matrixes to extract spatial correlation of a target station with all other stations classified as near neighbors, middle neighbors and distant neighbors. Respectively, it adopts three graph convolution network (GCN) and LSTM combined modules to capture the spatio-temporal influences from different neighbors. Finally, the outputs of the two components are fused with different weights to generate prediction. We evaluate Multi-STGCnet on a real world dataset from the metro system in Shenzhen, China. Experiment results demonstrate that our model outperforms multiple baselines.