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arXiv · 1905.10069

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

Lei Bai·Lina Yao·Salil. S Kanhere·Xianzhi Wang·Quan. Z Sheng
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Abstract

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.

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Lei Bai, Lina Yao, Salil. S Kanhere, Xianzhi Wang, Quan. Z Sheng. 2019-05-24. STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. https://arxiv.org/abs/1905.10069

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