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dc.contributor.authorMeng, Qiang
dc.contributor.authorQu, Xiaobo
dc.date.accessioned2017-05-03T16:09:51Z
dc.date.available2017-05-03T16:09:51Z
dc.date.issued2012
dc.date.modified2013-06-03T00:38:57Z
dc.identifier.issn0001-4575
dc.identifier.doi10.1016/j.aap.2012.01.025
dc.identifier.urihttp://hdl.handle.net/10072/47097
dc.description.abstractAccording to The Handbook of Tunnel Fire Safety, over 90% (55 out of 61 cases) of fires in road tunnels are caused by vehicle crashes (especially rear-end crashes). It is thus important to develop a proper methodology that is able to estimate the rear-end vehicle crash frequency in road tunnels. In this paper, we first analyze the time to collision (TTC) data collected from two road tunnels of Singapore and conclude that Inverse Gaussian distribution is the best-fitted distribution to the TTC data. An Inverse Gaussian regression model is hence used to establish the relationship between the TTC and its contributing factors. We then proceed to introduce a new concept of exposure to traffic conflicts as the mean sojourn time in a given time period that vehicles are exposed to dangerous scenarios, namely, the TTC is lower than a predetermined threshold value. We further establish the relationship between the proposed exposure to traffic conflicts and crash count by using negative binomial regression models. Based on the limited data samples used in this study, the negative binomial regression models perform well although a further study using more data is needed.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent273656 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom254
dc.relation.ispartofpageto263
dc.relation.ispartofjournalAccident Analysis and Prevention
dc.relation.ispartofvolume48
dc.rights.retentionY
dc.subject.fieldofresearchTransport engineering
dc.subject.fieldofresearchTransportation, logistics and supply chains
dc.subject.fieldofresearchcode400512
dc.subject.fieldofresearchcode3509
dc.titleEstimation of rear-end vehicle crash frequencies in urban road tunnels
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2012 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorQu, Xiaobo


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