Nonlinear autoregressive conditional duration models for traffic congestion estimation

Vlahogianni, EI; Karlaftis, MG; Kepaptsoglou, K

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Kepaptsoglou, K	en
dc.date.accessioned	2014-03-01T02:03:02Z
dc.date.available	2014-03-01T02:03:02Z
dc.date.issued	2011	en
dc.identifier.issn	1687952X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29339
dc.title	Nonlinear autoregressive conditional duration models for traffic congestion estimation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1155/2011/798953	en
heal.identifier.secondary	http://dx.doi.org/10.1155/2011/798953	en
heal.identifier.secondary	798953	en
heal.publicationDate	2011	en
heal.abstract	The considerable impact of congestion on transportation networks is reflected by the vast amount of research papers dedicated to congestion identification, modeling, and alleviation. Despite this, the statistical characteristics of congestion, and particularly of its duration, have not been systematically studied, regardless of the fact that they can offer significant insights on its formation, effects and alleviation. We extend previous research by proposing the autoregressive conditional duration (ACD) approach for modeling congestion duration in urban signalized arterials. Results based on data from a signalized arterial indicate that a multiregime nonlinear ACD model best describes the observed congestion duration data while when it lasts longer than 18 minutes, traffic exhibits persistence and slow recovery rate. Copyright © 2011 Eleni I. Vlahogianni et al.	en
heal.journalName	Journal of Probability and Statistics	en
dc.identifier.doi	10.1155/2011/798953	en