Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction.

Oluwasanmi, A; Aftab, MU; Qin, Z; Sarfraz, MS; Yu, Y; Rauf, HT

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction.

Oluwasanmi, A Aftab, MU Qin, Z Sarfraz, MS Yu, Y

Rauf, HT

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2023, 23, (8), pp. 3836
Issue Date:: 2023-04-09

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Field	Value	Language
dc.contributor.author	Oluwasanmi, A
dc.contributor.author	Aftab, MU
dc.contributor.author	Qin, Z
dc.contributor.author	Sarfraz, MS
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Rauf, HT
dc.date.accessioned	2024-01-18T23:00:58Z
dc.date.available	2023-04-04
dc.date.available	2024-01-18T23:00:58Z
dc.date.issued	2023-04-09
dc.identifier.citation	Sensors (Basel), 2023, 23, (8), pp. 3836
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/174781
dc.description.abstract	Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s23083836
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	3103 Ecology
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4104 Environmental management
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction.
dc.type	Journal Article
utslib.citation.volume	23
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-18T23:00:52Z
pubs.issue	8
pubs.publication-status	Published online
pubs.volume	23
utslib.citation.issue	8

Abstract:

Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.

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http://hdl.handle.net/10453/174781