  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. Specifically, DMVST-VGNN integrates the structures of 1D
Convolutional Neural Network, Multi Graph Attention Neural Network and
Transformer layer, which correspond to short-term temporal dynamics view,
spatial dynamics view and long-term temporal dynamics view respectively. In
this paper, experiments are conducted on two large-scale New York City datasets
in fine-grained prediction scenes. And the experimental results demonstrate
effectiveness and superiority of DMVST-VGNN framework in significant citywide
ride-hailing demand prediction.
