A Unified STARIMA based Model for Short-term Traffic Flow Prediction

Duan, P; Mao, G; Yue, W; Wang, S

A Unified STARIMA based Model for Short-term Traffic Flow Prediction

Duan, P Mao, G

Yue, W Wang, S

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018, 2018-November pp. 1652 - 1657
Issue Date:: 2018-12-07

Closed Access

	Filename	Description	Size
	08569964.pdf	Published version	864 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Duan, P	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.contributor.author	Yue, W	en_US
dc.contributor.author	Wang, S https://orcid.org/0000-0003-4347-6669	en_US
dc.date.issued	2018-12-07	en_US
dc.identifier.citation	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018, 2018-November pp. 1652 - 1657	en_US
dc.identifier.isbn	9781728103235	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133468
dc.description.abstract	© 2018 IEEE. This paper proposes a unified spatio-temporal model on the basis of STARIMA (Space-Time AutoRegressive Integrated Moving Average) for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amenable to suit changing road and traffic conditions. Second, unlike some existing techniques which capture the variation of spatio-temporal correlation by a complete redesign and calibration of the model, the proposed approach uses a unified model which incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two set of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used ARIMA (AutoRegressive Integrated Moving Average) and is only marginally inferior to that obtained by constructing multiple STARIMA models for different time of the day, however with a much reduced computational and implementation complexity.	en_US
dc.relation.ispartof	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC	en_US
dc.relation.isbasedon	10.1109/ITSC.2018.8569964	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Unified STARIMA based Model for Short-term Traffic Flow Prediction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-November	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-November	en_US

Abstract:

© 2018 IEEE. This paper proposes a unified spatio-temporal model on the basis of STARIMA (Space-Time AutoRegressive Integrated Moving Average) for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amenable to suit changing road and traffic conditions. Second, unlike some existing techniques which capture the variation of spatio-temporal correlation by a complete redesign and calibration of the model, the proposed approach uses a unified model which incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two set of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used ARIMA (AutoRegressive Integrated Moving Average) and is only marginally inferior to that obtained by constructing multiple STARIMA models for different time of the day, however with a much reduced computational and implementation complexity.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/133468