A multivariate state space approach for urban traffic flow modeling and prediction

Stathopoulos, A; Karlaftis, MG

dc.contributor.author	Stathopoulos, A	en
dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T01:18:32Z
dc.date.available	2014-03-01T01:18:32Z
dc.date.issued	2003	en
dc.identifier.issn	0968-090X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15073
dc.subject	Multivariate time series	en
dc.subject	Short-term predictions	en
dc.subject	Traffic flow	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Real time systems	en
dc.subject.other	State space methods	en
dc.subject.other	Time series analysis	en
dc.subject.other	Traffic control	en
dc.subject.other	Traffic congestion	en
dc.subject.other	Transportation	en
dc.subject.other	modeling	en
dc.subject.other	multivariate analysis	en
dc.subject.other	planning method	en
dc.subject.other	traffic management	en
dc.subject.other	urban planning	en
dc.title	A multivariate state space approach for urban traffic flow modeling and prediction	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0968-090X(03)00004-4	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0968-090X(03)00004-4	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Urban traffic congestion is one of the most severe problems of everyday life in Metropolitan areas. In an effort to deal with this problem, intelligent transportation systems (ITS) technologies have concentrated in recent years on dealing with urban congestion. One of the most critical aspects of ITS success is the provision of accurate real-time information and short-term predictions of traffic parameters such as traffic volumes, travel speeds and occupancies. The present paper concentrates on developing flexible and explicitly multivariate time-series state space models using core urban area loop detector data. Using 3-min volume measurements from urban arterial streets near downtown Athens, models were developed that feed on data from upstream detectors to improve on the predictions of downstream locations. The results clearly suggest that different model specifications are appropriate for different time periods of the day. Further, it also appears that the use of multivariate state space models improves on the prediction accuracy over univariate time series ones. (C) 2003 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Transportation Research Part C: Emerging Technologies	en
dc.identifier.doi	10.1016/S0968-090X(03)00004-4	en
dc.identifier.isi	ISI:000183237700002	en
dc.identifier.volume	11	en
dc.identifier.issue	2	en
dc.identifier.spage	121	en
dc.identifier.epage	135	en