Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14783
DC FieldValueLanguage
dc.contributor.authorGregoriades, Andreas-
dc.contributor.authorMouskos, Kyriacos C.-
dc.date.accessioned2019-08-01T11:48:28Z-
dc.date.available2019-08-01T11:48:28Z-
dc.date.issued2013-03-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2013, vol. 28, pp. 28-43en_US
dc.identifier.issn0968090X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/14783-
dc.description.abstractTraffic accidents constitute a major problem worldwide. One of the principal causes of traffic accidents is adverse driving behavior that is inherently influenced by traffic conditions and infrastructure among other parameters. Probabilistic models for the assessment of road accidents risk usually employs machine learning using historical data of accident records. The main drawback of these approaches is limited coverage of traffic data. This study illustrates a prototype approach that escapes from this problem, and highlights the need to enhance historical accident records with traffic information for improved road safety analysis. Traffic conditions estimation is achieved through Dynamic Traffic Assignment (DTA) simulation that utilizes temporal aspects of a transportation system. Accident risk quantification is achieved through a Bayesian Networks (BNs) model learned from the method's enriched accidents dataset. The study illustrates the integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTAs), for the assessment of accident risk index (ARI), used to identify accident black spots on road networks. .en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofTransportation Research Part C: Emerging Technologiesen_US
dc.rights© Elsevieren_US
dc.subjectAccident analysisen_US
dc.subjectBayesian Networksen_US
dc.subjectCrash analysisen_US
dc.subjectDynamic Traffic Assignmenten_US
dc.subjectRoad safetyen_US
dc.titleBlack spots identification through a Bayesian Networks quantification of accident risk indexen_US
dc.typeArticleen_US
dc.collaborationEuropean University Cyprusen_US
dc.collaborationThe City College of New Yorken_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.trc.2012.12.008en_US
dc.identifier.scopus2-s2.0-84873054062-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84873054062-
dc.relation.volume28en_US
cut.common.academicyear2012-2013en_US
dc.identifier.spage28en_US
dc.identifier.epage43en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn0968-090X-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-7422-1514-
crisitem.author.parentorgFaculty of Communication and Media Studies-
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