Huang, R. orcid.org/0000-0001-7545-0987, Chen, Z., Zhai, G. et al. (2 more authors) (2023) Spatial‐temporal correlation graph convolutional networks for traffic forecasting. IET Intelligent Transport Systems, 17 (5). pp. 628-655. ISSN 1751-956X
Abstract
Traffic forecasting, as a fundamental and challenging problem of intelligent transportation systems (ITS), has always been the focus of researchers. Nevertheless, accurate traffic forecasting still exists some problems due to the complex spatial-temporal dependencies and irregularities of traffic flows. Most of the existing methods typically use the spatial adjacency matrix and complicated mechanism to model spatial-temporal relationships separately, while ignoring the latent spatial-temporal correlations. In this paper, a novel architecture is proposed named spatial-temporal correlation graph convolutional networks (STCGCN) for traffic prediction. First, an informative fused graph structure is constructed to better learn the complex spatial-temporal correlations, which breaks the limitation that the general spatial adjacency matrix cannot reflect temporal correlations. Moreover, spatial-temporal correlation graph convolution and gated temporal convolution are performed in parallel and they are integrated into a unified layer, which enables capturing both local and global spatial-temporal dependencies simultaneously. By stacking multiple layers, STCGCN can learn more long-range spatial-temporal dependencies. Experimental results on five public traffic datasets demonstrate the effectiveness and robustness of the proposed STCGCN in urban traffic forecasting.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made ((https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | management and control; neural net architecture; network topology; traffic modeling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jan 2023 14:34 |
Last Modified: | 27 Sep 2024 11:59 |
Status: | Published |
Publisher: | Institution of Engineering and Technology (IET) |
Refereed: | Yes |
Identification Number: | 10.1049/itr2.12330 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195401 |