Prediction of Risk Generated by Different Driving Patterns and Their Conflict Redistribution

dc.authoridPeker, Ali Ufuk/0000-0003-1332-0305
dc.authoridAcarman, Tankut/0000-0003-4169-1189
dc.authorscopusid57195436606
dc.authorscopusid56027515200
dc.authorscopusid44461771900
dc.authorscopusid6602616551
dc.authorwosidPeker, Ali Ufuk/W-1417-2019
dc.authorwosidAcarman, Tankut/AAB-4894-2020
dc.contributor.authorGunduz, Gultekin
dc.contributor.authorYaman, Cagdas
dc.contributor.authorPeker, Ali Ufuk
dc.contributor.authorAcarman, Tankut
dc.date.accessioned2024-05-25T11:19:09Z
dc.date.available2024-05-25T11:19:09Z
dc.date.issued2018
dc.departmentOkan Universityen_US
dc.department-temp[Gunduz, Gultekin; Acarman, Tankut] Galatasaray Univ, Dept Comp Engn, TR-34349 Istanbul, Turkey; [Yaman, Cagdas] Infotech Commun & Informat Technol Inc, TR-34742 Istanbul, Turkey; [Peker, Ali Ufuk] Okan Univ, Dept Software Engn, TR-34959 Istanbul, Turkeyen_US
dc.descriptionPeker, Ali Ufuk/0000-0003-1332-0305; Acarman, Tankut/0000-0003-4169-1189en_US
dc.description.abstractIn this paper, risk level correlation and classification using belief functions based on driving activities' data about sharp transient maneuvering tasks, legal speed exceeding, and average speed ensuing with the human being who is controlling the technical system, i.e., the car, is presented. A dataset is constituted by time stamped and geographically referenced driving maneuver information, which is exceptionally reported when an acceleration exceeds the given threshold in both longitudinal and lateral direction. Risk level is labeled in terms of the change in total vehicle collision property damage cost for the analyzed time period, and long term driving activities' magnitude and frequency is divided into two equal time periods, which are used to predict risk levels. Redistribution of conflicts generated by driving activities is used to predict the future risk level of involvement in an accident. Using fuzzy approach a microaveraged F-measure 90.98% is achieved by Dubois-Parade and PCR6 conflict redistribution methods.en_US
dc.description.sponsorshipScientific Research Support Program of Galatasaray University [17.401.001]en_US
dc.description.sponsorshipThe work of G. Gunduz and T. Acarman was supported by the Scientific Research Support Program of Galatasaray University under Grant #17.401.001.en_US
dc.identifier.citation7
dc.identifier.doi10.1109/TIV.2017.2788203
dc.identifier.endpage80en_US
dc.identifier.issn2379-8858
dc.identifier.issn2379-8904
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85082633240
dc.identifier.scopusqualityQ1
dc.identifier.startpage71en_US
dc.identifier.urihttps://doi.org/10.1109/TIV.2017.2788203
dc.identifier.urihttps://hdl.handle.net/20.500.14517/370
dc.identifier.volume3en_US
dc.identifier.wosWOS:000722388100007
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherIeee-inst Electrical Electronics Engineers incen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayes methodsen_US
dc.subjectestimationen_US
dc.subjectmeasurement uncertaintyen_US
dc.subjectprobability density functionen_US
dc.subjectrisk analysisen_US
dc.subjectvehiclesen_US
dc.titlePrediction of Risk Generated by Different Driving Patterns and Their Conflict Redistributionen_US
dc.typeArticleen_US
dspace.entity.typePublication

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