An information fusion framework of traffic counts forecasts based on concepts from fuzzy set theory

Stathopoulos, A; Karlaftis, MG; Dimitriou, L

dc.contributor.author	Stathopoulos, A	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Dimitriou, L	en
dc.date.accessioned	2014-03-01T02:51:57Z
dc.date.available	2014-03-01T02:51:57Z
dc.date.issued	2009	en
dc.identifier.issn	14746670	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35766
dc.subject	Adaptive control strategies	en
dc.subject	Fussy sets theory	en
dc.subject	Information fusion	en
dc.subject	Time series	en
dc.subject	Traffic flow forecasting	en
dc.subject.other	Adaptive control strategy	en
dc.subject.other	Alternative approach	en
dc.subject.other	Expert knowledge	en
dc.subject.other	Information acquisitions	en
dc.subject.other	Sets theory	en
dc.subject.other	Traffic counts	en
dc.subject.other	Traffic flow forecasting	en
dc.subject.other	Traffic state	en
dc.subject.other	Traffic volumes	en
dc.subject.other	Transportation management	en
dc.subject.other	Urban networks	en
dc.subject.other	Advanced traffic management systems	en
dc.subject.other	Data fusion	en
dc.subject.other	Forecasting	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy set theory	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Information fusion	en
dc.subject.other	Information management	en
dc.subject.other	Time series	en
dc.subject.other	Traffic control	en
dc.subject.other	Advanced traveler information systems	en
dc.title	An information fusion framework of traffic counts forecasts based on concepts from fuzzy set theory	en
heal.type	conferenceItem	en
heal.identifier.primary	10.3182/20090902-3-US-2007.0022	en
heal.identifier.secondary	http://dx.doi.org/10.3182/20090902-3-US-2007.0022	en
heal.publicationDate	2009	en
heal.abstract	Reliable surveillance of urban networks coupled with techniques for information acquisition on traffic states provides the basis for the deployment of Advanced Transportation Management and Information Systems (ATMIS). Since information can be collected from various sources, a gamut of approaches for the fusion of available data has been utilized in traffic control centers. This paper focuses on a special paradigm of data fusion that combines information on forecasted traffic volume obtained from a variety of alternative approaches and provides a novel forecasting scheme that treats uncertainty by adopting concepts from fuzzy set theory and expert knowledge. © 2009 IFAC.	en
heal.journalName	IFAC Proceedings Volumes (IFAC-PapersOnline)	en
dc.identifier.doi	10.3182/20090902-3-US-2007.0022	en
dc.identifier.spage	278	en
dc.identifier.epage	285	en