research-article

Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network

Authors:

Zekun HeAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Pages 806 - 814

https://doi.org/10.1145/3437963.3441785

Published: 08 March 2021 Publication History

Abstract

Predicting crowd flows is crucial for urban planning, traffic management and public safety. However, predicting crowd flows is not trivial because of three challenges: 1) highly heterogeneous mobility data collected by various services; 2) complex spatio-temporal correlations of crowd flows, including multi-scale spatial correlations along with non-linear temporal correlations. 3) diversity in long-term temporal patterns. To tackle these challenges, we proposed an end-to-end architecture, called pyramid dilated spatial-temporal network (PDSTN), to effectively learn spatial-temporal representations of crowd flows with a novel attention mechanism. Specifically, PDSTN employs the ConvLSTM structure to identify complex features that capture spatial-temporal correlations simultaneously, and then stacks multiple ConvLSTM units for deeper feature extraction. For further improving the spatial learning ability, a pyramid dilated residual network is introduced by adopting several dilated residual ConvLSTM networks to extract multi-scale spatial information. In addition, a novel attention mechanism, which considers both long-term periodicity and the shift in periodicity, is designed to study diverse temporal patterns. Extensive experiments were conducted on three highly heterogeneous real-world mobility datasets to illustrate the effectiveness of PDSTN beyond the state-of-the-art methods. Moreover, PDSTN provides intuitive interpretation into the prediction.

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Cited By

Dai GKong WLiu YZhang BPeng XFan XHuang H(2024)Tucker Decomposition-Enhanced Dynamic Graph Convolutional Networks for Crowd Flows PredictionACM Transactions on Intelligent Systems and Technology10.1145/370611616:1(1-19)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3706116
Khoshraftar SAn A(2023)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3633518
Zhang XChowdhury RShang JGupta RHong DSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)ESC-GANProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498461(1347-1356)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498461
Show More Cited By

Index Terms

Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network

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cover image ACM Conferences

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Copyright © 2021 ACM.

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Published: 08 March 2021

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National Key Research and Development Program of China

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WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

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Cited By

Dai GKong WLiu YZhang BPeng XFan XHuang H(2024)Tucker Decomposition-Enhanced Dynamic Graph Convolutional Networks for Crowd Flows PredictionACM Transactions on Intelligent Systems and Technology10.1145/370611616:1(1-19)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3706116
Khoshraftar SAn A(2023)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3633518
Zhang XChowdhury RShang JGupta RHong DSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)ESC-GANProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498461(1347-1356)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498461
Xue HSalim FRen YClarke CSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Translating Human Mobility Forecasting through Natural Language GenerationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498387(1224-1233)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498387

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