Taxi Passenger Flow Prediction Based on Heterogeneous Spatiotemporal Graph Convolutional Networks
Accurately predicting regional taxi passenger flow plays an important role in taxi dispatch and passenger transporta-tion.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 explora-tion 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 mul-tiple target regions.To capture the spatiotemporal characteristics of passenger flow data,we construct a heterogeneous graph uti-lizing regional physical adjacency graphs,regional similarity graphs,and origin-destination(OD)correlation graphs.Further-more,based on these adjacency matrices,we build a dynamic graph reflecting the spatiotemporal dynamics of regions.We em-ploy 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.
万方数据
维普
