Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics

Vlahogianni, EI

dc.contributor.author	Vlahogianni, EI	en
dc.date.accessioned	2014-03-01T01:30:21Z
dc.date.available	2014-03-01T01:30:21Z
dc.date.issued	2009	en
dc.identifier.issn	1547-2450	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19561
dc.subject	Neural Networks	en
dc.subject	Pattern-based Prediction	en
dc.subject	Short-term Prediction	en
dc.subject	Traffic Flow	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Intelligent transportation systems	en
dc.subject.other	Pattern-based Prediction	en
dc.subject.other	Prediction schemes	en
dc.subject.other	Prediction techniques	en
dc.subject.other	Short-term Prediction	en
dc.subject.other	Short-term traffic flow	en
dc.subject.other	Traffic dynamics	en
dc.subject.other	Traffic Flow	en
dc.subject.other	Forecasting	en
dc.subject.other	Intelligent vehicle highway systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Vehicle locating systems	en
dc.subject.other	Traffic control	en
dc.subject.other	artificial neural network	en
dc.subject.other	information system	en
dc.subject.other	prediction	en
dc.subject.other	traffic congestion	en
dc.subject.other	traffic management	en
dc.title	Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/15472450902858384	en
heal.identifier.secondary	http://dx.doi.org/10.1080/15472450902858384	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Short-term traffic flow predictions are an essential part of intelligent transportation systems. Previous research underlines the difficulty in systematically assessing the predictability of traffic flow near capacity or during congested conditions. In this article a neural network prediction scheme is proposed that is consistent with the pattern-based evolution of traffic flow and has the capability of exploiting past information to acquire knowledge on the traffic dynamics in order to enhance predictability. Findings indicate that pattern-based predictions are more accuratein the traffic flow regimes consideredwhen compared to other local and global prediction techniques that operate under the time-series consideration. The pattern-based prediction scheme was also found to outperform the other methods tested in the knowledge of the anticipated traffic flow state in all traffic flow conditions considered.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Journal of Intelligent Transportation Systems: Technology, Planning, and Operations	en
dc.identifier.doi	10.1080/15472450902858384	en
dc.identifier.isi	ISI:000265646400002	en
dc.identifier.volume	13	en
dc.identifier.issue	2	en
dc.identifier.spage	73	en
dc.identifier.epage	84	en