Short-term predictability of traffic flow regimes in signalised arterials

Vlahogianni, EI

dc.contributor.author	Vlahogianni, EI	en
dc.date.accessioned	2014-03-01T01:29:07Z
dc.date.available	2014-03-01T01:29:07Z
dc.date.issued	2008	en
dc.identifier.issn	1037-5783	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19145
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-49749118001&partnerID=40&md5=c5fa3cfeb034255a12aa20ea4afd0f81	en
dc.subject.other	Administrative data processing	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Forecasting	en
dc.subject.other	Image classification	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Intelligent vehicle highway systems	en
dc.subject.other	Management information systems	en
dc.subject.other	Network management	en
dc.subject.other	Statistical methods	en
dc.subject.other	Traffic control	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Vegetation	en
dc.subject.other	Vehicle locating systems	en
dc.subject.other	Comparative studies	en
dc.subject.other	Critical flows	en
dc.subject.other	Flow regimes	en
dc.subject.other	Intelligent transportation systems	en
dc.subject.other	Modular neural network	en
dc.subject.other	Modular neural networks	en
dc.subject.other	Non-linear evolutions	en
dc.subject.other	Short-term traffic flow	en
dc.subject.other	Statistical characteristics	en
dc.subject.other	Statistical information	en
dc.subject.other	Traffic flow prediction	en
dc.subject.other	Traffic flowing	en
dc.subject.other	Neural networks	en
dc.title	Short-term predictability of traffic flow regimes in signalised arterials	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Modern intelligent transportation systems require the integration of accurate short-term traffic predictions for the purpose of enhanced network management. The present paper proposes a novel conceptual approach to traffic flow prediction based on the joint consideration of time-lagged volume and occupancy data, and information on the statistical characteristics of the short-term traffic flow evolution. For the purpose of prediction, a genetically optimised modular neural network architecture is developed. Findings indicate that the regimes in critical flow near capacity (where traffic flow exhibits unstable nonlinear evolution) are more difficult to predict when compared to the free-flow and jammed conditions. Moreover, the predictability of the critical-flow regimes decreases sharply in multiple-steps-ahead prediction, whereas the predictability of the regimes where traffic flow exhibits a more stable temporal behaviour is less affected by the expansion of the predictive horizon. Finally, a comparative study shows that the proposed modular neural network represents more accurately the anticipated traffic flow regimes when compared to the modular neural networks trained without the statistical information on the traffic flow evolution.	en
heal.publisher	ARRB GROUP LTD	en
heal.journalName	Road and Transport Research	en
dc.identifier.isi	ISI:000258628100002	en
dc.identifier.volume	17	en
dc.identifier.issue	2	en
dc.identifier.spage	19	en
dc.identifier.epage	33	en