Huang, Ru and Chen, Zijian and Zhai, Guangtao and He, Jianhua and Chu, Xiaoli (2023) Spatial‐temporal correlation graph convolutional networks for traffic forecasting. IET Intelligent Transport Systems, 17 (7). pp. 1380-1394. DOI https://doi.org/10.1049/itr2.12330
Huang, Ru and Chen, Zijian and Zhai, Guangtao and He, Jianhua and Chu, Xiaoli (2023) Spatial‐temporal correlation graph convolutional networks for traffic forecasting. IET Intelligent Transport Systems, 17 (7). pp. 1380-1394. DOI https://doi.org/10.1049/itr2.12330
Huang, Ru and Chen, Zijian and Zhai, Guangtao and He, Jianhua and Chu, Xiaoli (2023) Spatial‐temporal correlation graph convolutional networks for traffic forecasting. IET Intelligent Transport Systems, 17 (7). pp. 1380-1394. DOI https://doi.org/10.1049/itr2.12330
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | management and control; neural net architecture; network topology; traffic modeling |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Jun 2025 14:12 |
Last Modified: | 27 Jun 2025 14:13 |
URI: | http://repository.essex.ac.uk/id/eprint/37794 |
Available files
Filename: Spatial and temporal correlation graph convolutional networks for traffic prediction.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0