A traffic flow-forecasting model based on multi-head spatio–temporal attention and adaptive graph convolutional networks

Hong Zhang (), Sunan Kan, Jie Cao (), Linlong Chen () and Tianxin Zhao ()
Additional contact information
Hong Zhang: College of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Sunan Kan: College of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Jie Cao: College of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Linlong Chen: College of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China
Tianxin Zhao: College of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 10, 1-15

Abstract: Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but it is a challenging task due to the complex spatial-temporal dependence and uncertainty of traffic flow. In most existing approaches, spatial correlation is captured using graph convolution networks in a pre-determined graph structure. However, some nodes in such a graph structure have spatial correlations between them but are missing a connection, so the hidden spatial correlations between these nodes cannot be captured. Traffic flow has dynamic characteristics, showing different characteristics over time. Most methods ignore the dynamics of traffic flow when modeling the spatio–temporal correlation of traffic flow. We proposed a new network model (MSTA-GCN) to solve the above problem. The model presents a gated adaptive graph convolutional network that captures the hidden spatial correlations between graph nodes from the adaptive. In addition, the model introduces a multi-head spatial-temporal attention mechanism to pay attention to spatial-temporal information of different historical moments and different spatial dimensions to effectively capture the dynamics of spatial-temporal correlation of traffic flow. Extensive experiments were conducted on four datasets of PEMS. The experimental results show that the MSTA-GCN model has better forecasting performance compared with the baseline methods.

Keywords: Traffic flow forecasting; deep learning; multi-head spatio–temporal attention mechanism; graph convolutional network; spatio–temporal correlation (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183122501376
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:33:y:2022:i:10:n:s0129183122501376

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183122501376

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().