  Ride-hailing demand prediction is an essential task in spatial-temporal data
mining. Accurate Ride-hailing demand prediction can help to pre-allocate
resources, improve vehicle utilization and user experiences. Graph
Convolutional Networks (GCN) is commonly used to model the complicated
irregular non-Euclidean spatial correlations. However, existing GCN-based
ride-hailing demand prediction methods only assign the same importance to
different neighbor regions, and maintain a fixed graph structure with static
spatial relationships throughout the timeline when extracting the irregular
non-Euclidean spatial correlations. In this paper, we propose the
Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing
demand prediction method. Based on the attention mechanism of GAT, STDGAT
extracts different pair-wise correlations to achieve the adaptive importance
allocation for different neighbor regions. Moreover, in STDGAT, we design a
novel time-specific commuting-based graph attention mode to construct a dynamic
graph structure for capturing the dynamic time-specific spatial relationships
throughout the timeline. Extensive experiments are conducted on a real-world
ride-hailing demand dataset, and the experimental results demonstrate the
significant improvement of our method on three evaluation metrics RMSE, MAPE
and MAE over state-of-the-art baselines.
