Highlights • A multi-community spatio-temporal graph convolutional network is proposed to predict passenger demand. • The spatial correlations among regions are encoded through the graph convolutional network. • The Louvain algorithm is used to accomplish the passenger demand prediction of multi-regions. • The taxi order data collected in two cities are used in model validation and comparison. • The experiment result illustrates the superiority of the proposed model.

Abstract Region-level passenger demand prediction plays an important role in the coordination of travel demand and supply in the urban public transportation system. The complex urban road network structure leads to irregular shapes and arrangements of regions, which poses a challenge for capturing the spatio-temporal correlation of demand generated in different regions. In this study, we propose a multi-community spatio-temporal graph convolutional network (MC_STGCN) framework to predict passenger demand at a multi-region level by exploring spatio-temporal correlations among regions. Specifically, the gated recurrent unit (GRU) is applied to encode the temporal correlation in regions into a vector. On the other hand, the spatial correlations among regions are encoded into two graphs through the graph convolutional network (GCN): geographically adjacent graph and functional similarity graph. Then, a prediction module based on the Louvain algorithm is used to accomplish the passenger demand prediction of multi-regions. The two real-world taxi order data collected in Shenzhen City and New York City are used in model validation and comparison. The numerical results show that the MC_STGCN model outperforms both classical time-series prediction methods and deep learning approaches. Moreover, in order to better illustrate the superiority of the proposed model, we further discuss the improvement of prediction performance though spatio-temporal correlation modeling and analyzing, the effectiveness of community detection compared with random classification of regions, and the advantages of regional level prediction compared with grid-based prediction models.