Empirical and analytical investigation of traffic flow regimes and transitions in signalized arterials

Vlahogianni, EI; Geroliminis, N; Skabardonis, A

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Geroliminis, N	en
dc.contributor.author	Skabardonis, A	en
dc.date.accessioned	2014-03-01T01:28:15Z
dc.date.available	2014-03-01T01:28:15Z
dc.date.issued	2008	en
dc.identifier.issn	0733-947X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18780
dc.subject	Bayesian analysis	en
dc.subject	Fuzzy sets	en
dc.subject	Kinematic wave theory	en
dc.subject	Time series analysis	en
dc.subject	Traffic analysis	en
dc.subject	Traffic models	en
dc.subject	Traffic signals	en
dc.subject	Wavelet	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Boundary value problems	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Inference engines	en
dc.subject.other	Kinematics	en
dc.subject.other	Speech recognition	en
dc.subject.other	Time series analysis	en
dc.subject.other	Traffic signals	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Bayesian analysis	en
dc.subject.other	Kinematic wave theory	en
dc.subject.other	Traffic analysis	en
dc.subject.other	Traffic models	en
dc.subject.other	Wavelet	en
dc.subject.other	Computer networks	en
dc.subject.other	Bayesian analysis	en
dc.subject.other	boundary condition	en
dc.subject.other	empirical analysis	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	planning method	en
dc.subject.other	time series analysis	en
dc.subject.other	traffic management	en
dc.title	Empirical and analytical investigation of traffic flow regimes and transitions in signalized arterials	en
heal.type	journalArticle	en
heal.identifier.primary	10.1061/(ASCE)0733-947X(2008)134:12(512)	en
heal.identifier.secondary	http://dx.doi.org/10.1061/(ASCE)0733-947X(2008)134:12(512)	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	The paper presents a methodological framework that integrates different data-driven techniques in order to detect the different traffic flow regimes (free flow, congested conditions, and so on) observed in signalized arterials as well as the manner traffic flow shifts from one regime to another (transitions). Traffic flow is determined by the joint consideration of the temporal evolution of volume and occupancy. The boundary conditions of the different regimes are identified via a fuzzy wavelet approach based on the volume-occupancy relationship. Moreover, a Bayesian network is developed in order to discover hidden associations between the observed shifts and the traffic flow conditions they occur. Results from the data-driven approach indicate the existence of four distinct traffic flow regimes; these regimes hold in arterials with different geometric and signalization characteristics. Finally, results are further discussed via an analytical model based on the kinematic wave theory; the comparative study of both approaches provides strong evidence that the presented statistical framework is in agreement with a simple and elegant analysis including traffic parameters that are observable and measurable. © 2008 ASCE.	en
heal.publisher	ASCE-AMER SOC CIVIL ENGINEERS	en
heal.journalName	Journal of Transportation Engineering	en
dc.identifier.doi	10.1061/(ASCE)0733-947X(2008)134:12(512)	en
dc.identifier.isi	ISI:000260940500003	en
dc.identifier.volume	134	en
dc.identifier.issue	12	en
dc.identifier.spage	512	en
dc.identifier.epage	522	en