StG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting

Publication Type:
Conference Proceeding
IJCAI International Joint Conference on Artificial Intelligence, 2019, 2019-August pp. 1981 - 1987
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© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. 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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