STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting
Abstract
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.
Cite
Text
Bai et al. "STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/274Markdown
[Bai et al. "STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/bai2019ijcai-stg/) doi:10.24963/IJCAI.2019/274BibTeX
@inproceedings{bai2019ijcai-stg,
title = {{STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting}},
author = {Bai, Lei and Yao, Lina and Kanhere, Salil S. and Wang, Xianzhi and Sheng, Quan Z.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {1981-1987},
doi = {10.24963/IJCAI.2019/274},
url = {https://mlanthology.org/ijcai/2019/bai2019ijcai-stg/}
}