FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction

Duan, Y; Chen, N; Shen, S; Zhang, P; Qu, Y; Yu, S

FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction

Duan, Y Chen, N Shen, S Zhang, P Qu, Y Yu, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2022, 71, (9), pp. 9250-9260
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Duan, Y
dc.contributor.author	Chen, N
dc.contributor.author	Shen, S
dc.contributor.author	Zhang, P
dc.contributor.author	Qu, Y
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2023-04-11T02:00:08Z
dc.date.available	2023-04-11T02:00:08Z
dc.date.issued	2022-09-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2022, 71, (9), pp. 9250-9260
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/169496
dc.description.abstract	With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2022.3178094
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction
dc.type	Journal Article
utslib.citation.volume	71
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-11T02:00:07Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	71
utslib.citation.issue	9

Abstract:

With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).

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http://hdl.handle.net/10453/169496