Ridesplitting demand prediction via spatiotemporal multi-graph convolutional network

Ridesplitting demand prediction plays an important role in vehicle scheduling and intelligent transportation system construction. Accurate ridesplitting demand prediction is crucial for alleviating supply-demand imbalance and increasing vehicle utilization. Ridesplitting refers to the sharing of a vehicle by multiple passengers with similar routes, thereby achieving a reduction in the adverse effects of increased congestion caused by ridehailing. However, most existing works mainly address the region-based ride-hailing demand prediction, whereas few works focus on the ridesplitting demand prediction, which takes into account the correlation between shared orders. To address the issue, we develop several intersection-based graphs to character the ridesplitting demand correlations between the intersections. The multi-graph convolutional networks (MGCN) with different graph combinations are constructed to capture the spatial topologies. Furthermore, we introduce the probabilistic model structure to predict the uncertainty of ridesplitting demand. A novel deep learning model, referred to as the Multi-graph Convolutional Gated Recurrent Unit with Probabilistic prediction (MGCGRU-P) is proposed. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Beijing, China). The results demonstrate that the model we propose performs the best against baseline models.