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dc.contributor.authorJin, Shengen_US
dc.contributor.authorQu, Xiaoboen_US
dc.contributor.authorWang, Dianhaien_US
dc.date.accessioned2017-05-03T16:09:51Z
dc.date.available2017-05-03T16:09:51Z
dc.date.issued2011en_US
dc.date.modified2012-04-15T22:32:02Z
dc.identifier.issn18756883en_US
dc.identifier.doi10.2991/ijcis.2011.4.6.4en_US
dc.identifier.urihttp://hdl.handle.net/10072/44478
dc.description.abstractTraffic safety is of great significance, especially in urban expressway where traffic volume is large and traffic conflicts are highlighted. It is thus important to develop a methodology that is able to assess traffic safety. In this paper, we first analyze the time to collision (TTC) samples from traffic videos collected from Beijing expressway with different locations, lanes, and traffic conditions. Accordingly, some basic descriptive statistics of 5 locations' TTC samples are shown, and it is concluded that Gaussian mixture model (GMM) distribution is the best-fitted distribution to TTC samples based on K-S goodness of fit tests. Using GMM distribution, TTC samples can be divided into three categories: dangerous situations, relative safe situations, and absolute safe situations, respectively. We then proceeds to introduce a novel concept of the percentage of serious traffic conflicts as the percentage of TTC samples below a predetermined threshold value in dangerous situation. After that, assessment results of expressway traffic safety are presented using the proposed traffic safety indictor. The results imply that traffic safety on the weaving segment is lower than that on mainlines and the percentage of serious traffic conflicts on median lane is larger than that on middle lane and shoulder lane.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherAtlantis Pressen_US
dc.publisher.placeFranceen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom1122en_US
dc.relation.ispartofpageto1130en_US
dc.relation.ispartofissue6en_US
dc.relation.ispartofjournalInternational Journal of Computational Intelligence Systemsen_US
dc.relation.ispartofvolume4en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchTransport Engineeringen_US
dc.subject.fieldofresearchcode090507en_US
dc.titleAssessment of Expressway Traffic Safety Using Gaussian Mixture Model based on Time to Collisionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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