STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

2019-05-24 · Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Quan Z. Sheng

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons 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.

📄 PDF Abstract BibTeX arXiv:1905.10069

Code (1)

LeiBAI/STG2Seq tf

Tasks

Demand ForecastingGraph-to-Sequence Multivariate Time Series Forecasting Prediction Time Series Forecasting Time Series Prediction Traffic Prediction

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