Risk-Aware Intelligent Dispatching for Ride-Hailing Systems Using Dual-Graph Attention Forecasting and Unbalanced Optimal Transport

Abstract

Ride-hailing dispatching with mixed on-demand and advance requests faces several challenges: platforms must respond to immediate requests, preserve flexibility for upcoming advance requests, and consider how each accepted request changes post-drop-off vehicle positions and subsequent service opportunities. Existing studies usually deal with demand forecasting and dispatching separately. Forecasting models mainly optimize order-count errors, and dispatching models usually focus on pickup distance, revenue, and waiting time, with limited consideration of destination opportunity and risk. To alleviate this prediction–dispatch disconnection, this paper proposes a risk-aware online dispatching method. The method develops a closed-loop context-aware spatio-temporal multi-task network (CC-STMT) using a physical-distance graph, a functional–semantic graph, and en-route supply feedback to estimate demand intensity, low-opportunity risk, and demand dispersion. Then, the demand-prediction-guided optimal transport matching (DP-OTM) method, which embeds destination opportunities and risks into the vehicle–request matching cost to generate an executable one-to-one allocation, is designed. Compared with the best-performing non-CC-STMT baseline for each metric, CC-STMT reduces MAE by 1.38%, 1.65%, and 7.65% and RMSE by 2.70%, 3.92%, and 5.14% in NYC, Chicago-A, and Chicago-B, respectively. Compared with Base-UOT, DP-OTM reduces future low-opportunity drop-offs and improves post-drop-off destination opportunity. These results support destination-aware dispatching in public-data simulations.