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

arXiv: https://arxiv.org/abs/2006.05905 · HTML: https://mlsanity.com/paper/2006.05905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing](https://mlsanity.com/paper/2207.04709/) (2022)
- [Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web](https://mlsanity.com/paper/2602.10502/) (2026)
- [VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration](https://mlsanity.com/paper/2409.19258/) (2024)
- [ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting](https://mlsanity.com/paper/2506.02576/) (2025)

## Abstract (arXiv)

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.