research-article

Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks

Authors:

Zeng ZengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 42, Pages 1 - 23

https://doi.org/10.1145/3385414

Published: 30 May 2020 Publication History

Abstract

Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.

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Index Terms

Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Publication History

Published: 30 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 July 2019

Published in TKDD Volume 14, Issue 4

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International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China
National Natural Science Foundation of China
Postdoctoral Science Foundation of China
National Key R8D Program of China
National Outstanding Youth Science Program of National Natural Science Foundation of China

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https://doi.org/10.1145/3711818
Zhuang HFang DTong KLiu YZeng ZZhou XChen C(2025)Online Analytic Exemplar-Free Continual Learning With Large Models for Imbalanced Autonomous Driving TaskIEEE Transactions on Vehicular Technology10.1109/TVT.2024.348355774:2(1949-1958)Online publication date: Feb-2025
https://doi.org/10.1109/TVT.2024.3483557
Yu XZhang KLiu YZou BWang JWang WQian R(2025)Adversarial Transformer-Based Anomaly Detection for Multivariate Time SeriesIEEE Transactions on Industrial Informatics10.1109/TII.2024.350721121:3(2471-2480)Online publication date: Mar-2025
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