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Ride-hailing demand prediction is an essential task in spatial-temporal datamining. Accurate Ride-hailing demand prediction can help to pre-allocateresources, improve vehicle utilization and user experiences. GraphConvolutional Networks (GCN) is commonly used to model the complicatedirregular non-Euclidean spatial correlations. However, existing GCN-basedride-hailing demand prediction methods only assign the same importance todifferent neighbor regions, and maintain a fixed graph structure with staticspatial relationships throughout the timeline when extracting the irregularnon-Euclidean spatial correlations. In this paper, we propose theSpatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailingdemand prediction method. Based on the attention mechanism of GAT, STDGATextracts different pair-wise correlations to achieve the adaptive importanceallocation for different neighbor regions. Moreover, in STDGAT, we design anovel time-specific commuting-based graph attention mode to construct a dynamicgraph structure for capturing the dynamic time-specific spatial relationshipsthroughout the timeline. Extensive experiments are conducted on a real-worldride-hailing demand dataset, and the experimental results demonstrate thesignificant improvement of our method on three evaluation metrics RMSE, MAPEand MAE over state-of-the-art baselines.

Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction