Abstract:  Accurately predicting regional taxi passenger flow plays an important role in taxi dispatch and passenger transportation. The exploration of spatiotemporal correlations in passenger flow is a critical factor in enhancing prediction accuracy. In light of the limited investigation into the spatiotemporal characteristics of regional passenger flow， particularly the inadequate exploration of passenger flow similarities between non-adjacent regions and the underexplored spatial relationships among regions， a Heterogeneous Spatio-Temporal Graph Convolutional Network （HSTGCN） is proposed to predict the passenger flow across multiple target regions. To capture the spatiotemporal characteristics of passenger flow data， we construct a heterogeneous graph utilizing regional physical adjacency graphs， regional similarity graphs， and origin-destination （OD） correlation graphs. Furthermore， based on these adjacency matrices， we build a dynamic graph reflecting the spatiotemporal dynamics of regions. We employ heterogeneous spatiotemporal graph convolutional networks to extract the spatiotemporal features of the data. Experimental results on publicly available datasets demonstrate that the model’s prediction outcomes outperform comparative models in terms of mean absolute error， root mean square error， accuracy， and R2， showcasing superior prediction accuracy. 

Key words:  , taxi dispatch； passenger flow prediction； spatial-temporal features； graph convolutional network； GRU ,