research-article

Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks

Authors:

Jiannong CaoAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 23, Issue 5

Pages 4695 - 4705

https://doi.org/10.1109/TITS.2021.3055207

Published: 01 May 2022 Publication History

Abstract

Accurately predicting the urban spatio-temporal data is critically important to various urban computing tasks for smart city related applications such as crowd flow prediction and traffic congestion prediction. Existing models especially deep learning based approaches require a large volume of training data, whose performance may degrade remarkably when the data is scarce. Recent works try to transfer knowledge from the intra-city or cross-city multi-modal spatio-temporal data. However, the careful design of what to transfer and how between the multi-modal spatio-temporal data needs to be determined in advance. There still lacks an end-to-end solution that can automatically capture the common cross-domain knowledge. In this paper, we propose a <underline>D</underline>eep <underline>A</underline>ttentive <underline>A</underline>daptation <underline>N</underline>etwork model named ST-DAAN to transfer cross-domain <underline>S</underline>patio-<underline>T</underline>emporal knowledge for urban crowd flow prediction. ST-DAAN first maps the raw spatio-temporal data of source domain and target domain to a common embedding space. Then domain adaptation is adopted on several domain-specific layers through adding a domain discrepancy penalty to explicitly match the mean embeddings of the two domain distributions. Considering the complex spatial correlation in many urban spatio-temporal data, a global attention mechanism is also designed to enable the model to capture broader spatial dependencies. Using urban crowd flow prediction as a demonstration, we conduct experiments on five real-world large datasets over both intra- and cross-city transfer learning. The results demonstrate that ST-DAAN outperforms state-of-the-art methods by a large margin.

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Cited By

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Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 23, Issue 5

May 2022

1001 pages

ISSN:1524-9050

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1558-0016 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 May 2022

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Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3333824
Ouyang XYang YZhou WZhang YWang HHuang W(2024)CityTrans: Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across CitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328352036:1(62-76)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3283520
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https://dl.acm.org/doi/10.1109/TITS.2024.3355143
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Shi GZhou QGu J(2024)Exploring Idealized Regional Match for Cross-City Cross-Mode Traffic Flow PredictionDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_4(54-69)Online publication date: 2-Jul-2024
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Wang HXia JYang YWang SCao J(2024)STS2ANet: Spatio-Temporal Synchronized Sliding Attention Network for Accurate Cross-Day Origin-Destination PredictionDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_12(186-202)Online publication date: 2-Jul-2024
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Mo JGong Z(2023)Cross-City Multi-Granular Adaptive Transfer Learning for Traffic Flow PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323218535:11(11246-11258)Online publication date: 1-Nov-2023
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