Identifying crash type propensity using real-time traffic data on freeways

Christoforou, Z; Cohen, S; Karlaftis, MG

dc.contributor.author	Christoforou, Z	en
dc.contributor.author	Cohen, S	en
dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T01:35:50Z
dc.date.available	2014-03-01T01:35:50Z
dc.date.issued	2011	en
dc.identifier.issn	0022-4375	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21209
dc.subject	Crash type	en
dc.subject	Freeway	en
dc.subject	Multivariate Probit	en
dc.subject	Traffic accident	en
dc.subject.classification	Ergonomics	en
dc.subject.classification	Public, Environmental & Occupational Health	en
dc.subject.classification	Social Sciences, Interdisciplinary	en
dc.subject.classification	Transportation	en
dc.subject.other	Crash type	en
dc.subject.other	Empirical findings	en
dc.subject.other	Freeway	en
dc.subject.other	Highway sections	en
dc.subject.other	Multivariate Probit	en
dc.subject.other	Probit models	en
dc.subject.other	Real time traffics	en
dc.subject.other	Real-time traffic datum	en
dc.subject.other	Rear-end crashes	en
dc.subject.other	Road crash	en
dc.subject.other	Traffic accidents	en
dc.subject.other	Traffic conditions	en
dc.subject.other	Traffic parameters	en
dc.subject.other	Vehicle crashes	en
dc.subject.other	Mathematical models	en
dc.subject.other	Highway accidents	en
dc.title	Identifying crash type propensity using real-time traffic data on freeways	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jsr.2011.01.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jsr.2011.01.001	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	Introduction: We examine the effects of various traffic parameters on type of road crash. Method: Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Results: Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent. Impact on Industry: Results could be integrated in a real-time traffic management application. (C) 2011 National Safety Council and Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Journal of Safety Research	en
dc.identifier.doi	10.1016/j.jsr.2011.01.001	en
dc.identifier.isi	ISI:000289021300007	en
dc.identifier.volume	42	en
dc.identifier.issue	1	en
dc.identifier.spage	43	en
dc.identifier.epage	50	en