Travel Demand Prediction Based on Multi-graph Spatio-temporal Attention Network
LI Ruimeng
WANG Meng
MA Yuzhe
Abstract：Passenger demand prediction is a crucial but challenging task for intelligent transportation system construction.In this paper,a multi-graph attention spatiotemporal prediction model is proposed.LSTM is used to model the context information of temporal dependence in travel records,and then three graphs are used to model multiple correlations of spatial regions,and GAT is used to capture the spatial dependencies between regions.In addition,weather information is integrated into the model to realize the global prediction of travel demand.Finally,a real data set is used to verify the validity of the proposed model.
Keywords：passenger demand predictionintelligent transportationspatio-temporal prediction
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3149-3154 )