A Travel Demand Prediction Model for Ride-Hailing Services Based on Spatio-Temporal Attention Mechanism
The paper aims to solve the problem of forecasting passenger travel demand in e-hailing car operations,thereby reducing vehicle idle rates and minimizing passenger waiting times.Considering the dynamic spatiotemporal dependencies of passenger travel demand,this study proposes a method based on spatial data visualization and the Granger causality test for analyzing the spatial dependency.A spatiotemporal graph convolutional neural network model incorporating attention mechanisms is established to predict passenger travel demand.The case study shows that this model effectively captures the dynamic characteristics of the time-space dependencies of passenger travel demand,improves the prediction performance of the model,and achieves high accuracy and practicability.
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