STARIMA-based traffic prediction with time-varying lags

Duan, P; Mao, G; Zhang, C; Wang, S

STARIMA-based traffic prediction with time-varying lags

Duan, P Mao, G

Zhang, C Wang, S

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Publication Type:: Conference Proceeding
Citation:: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2016, pp. 1610 - 1615
Issue Date:: 2016-12-22

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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	Zhang, C	en_US
dc.contributor.author	Wang, S https://orcid.org/0000-0003-4347-6669	en_US
dc.date.issued	2016-12-22	en_US
dc.identifier.citation	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2016, pp. 1610 - 1615	en_US
dc.identifier.isbn	9781509018895	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122852
dc.description.abstract	© 2016 IEEE. Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-Term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (timevarying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMAbased model with time-varying lags is developed for shortterm traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.	en_US
dc.relation.ispartof	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC	en_US
dc.relation.isbasedon	10.1109/ITSC.2016.7795773	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	STARIMA-based traffic prediction with time-varying lags	en_US
dc.type	Conference Proceeding
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

Abstract:

© 2016 IEEE. Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-Term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (timevarying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMAbased model with time-varying lags is developed for shortterm traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.

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http://hdl.handle.net/10453/122852