Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction

Understand

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction.

• Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch.
• Conventional deep learning methods with no external structured data can be accomplished via hybrid models of CNNs and RNNs by meshing plentiful pixel-level labeled data, but spatial data sparsity and limited learning capabilities on temporal long-term dependencies are still two striking bottlenecks.
• To address these limitations, we propose a new virtual graph modeling method to focus on significant demand regions and a novel Deep Multi-View Spatiotemporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen learning capabilities of spatial dynamics and temporal long-term dependencies.