Hidden Markov modeling and macroscopic traffic filtering supporting location-based services

Stamoulakatos, TS; Sykas, ED

dc.contributor.author	Stamoulakatos, TS	en
dc.contributor.author	Sykas, ED	en
dc.date.accessioned	2014-03-01T01:26:25Z
dc.date.available	2014-03-01T01:26:25Z
dc.date.issued	2007	en
dc.identifier.issn	1530-8669	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18070
dc.subject	Clustering	en
dc.subject	Hidden Markov model	en
dc.subject	Location based services	en
dc.subject	Pattern recognition	en
dc.subject	Traffic information	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Base stations	en
dc.subject.other	Global system for mobile communications	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Telecommunication services	en
dc.subject.other	Cellular signaling	en
dc.subject.other	Clustering large applications (CLARA)	en
dc.subject.other	Location-based services	en
dc.subject.other	Mobile terminal (MT)	en
dc.subject.other	Traffic information	en
dc.subject.other	Velocity estimation	en
dc.subject.other	Telecommunication traffic	en
dc.title	Hidden Markov modeling and macroscopic traffic filtering supporting location-based services	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/wcm.350	en
heal.identifier.secondary	http://dx.doi.org/10.1002/wcm.350	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this study, we present a technique that combines pattern recognition techniques with cellular signaling measurements and more precisely information extracted from Abis air interface in GSM network. The pattern recognition is applied to measurement reports that mobile terminal (MT) sends to its serving base station (BS). Modeling of these reports is performed by hidden Markov model (HMM) while employing clustering large applications (CLARA) as clustering method. The accurate results during MT velocity estimation located inside a probe vehicle show the potential of the method when applied to large scale of MTs in order to estimate basic parameters for road traffic. Copyright (c) 2006 John Wiley & Sons, Ltd.	en
heal.publisher	JOHN WILEY & SONS INC	en
heal.journalName	Wireless Communications and Mobile Computing	en
dc.identifier.doi	10.1002/wcm.350	en
dc.identifier.isi	ISI:000245968100002	en
dc.identifier.volume	7	en
dc.identifier.issue	4	en
dc.identifier.spage	415	en
dc.identifier.epage	429	en