Cognitive Vehicular Network Based Accident Information System for Sustainable Traffic

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dc.contributor.authorBesli, Muhammed Ali
dc.contributor.authorBayrakdar, Muhammed Enes
dc.date.accessioned2025-10-11T20:48:22Z
dc.date.available2025-10-11T20:48:22Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractVehicle Ad Hoc Networks emerge as a new research area. Vehicle ad hoc networks (VANETs) are widely used in intelligent transportation systems to reduce traffic congestion as well as to ensure safety and security by using vehicle-to-vehicle(V2 V) and vehicle-to-roadside(V2R) unit communications. Many people are seriously injured or even die in traffic accidents due to human errors, including driver errors (e.g. driver inattention and distraction, careless driving and poor driving skills) and errors of other road users (e.g. traffic violations). According to T & Uuml;& Idot;K data, the fact that the ratio of driver fault to total fault has never fallen below 88% for decades shows that this problem has become a chronic problem and needs to be thought about and solution(s) produced. The main factor that increases the possibility of drivers making mistakes is the roads they navigate with the help of navigation that they have not experienced before. At this point, a driver traveling on a road he has not experienced before should be warned as he approaches the point where many accidents have occurred on the relevant route before. By warning the driver who is approaching accident black spots, where many accidents have occurred in the past, more attention can be paid to points with high accident risk. Radius information of accident black spots can be estimated using a fuzzy-based model. As a result, the aim is to minimize accidents caused by driver error. This is done with the Accident Information System (KAB & Idot;S) application.en_US
dc.identifier.doi10.1080/03772063.2024.2448581
dc.identifier.endpage802en_US
dc.identifier.issn0377-2063
dc.identifier.issn0974-780X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-105002265407en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage782en_US
dc.identifier.urihttps://doi.org/10.1080/03772063.2024.2448581
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21884
dc.identifier.volume71en_US
dc.identifier.wosWOS:001391161600001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofIete Journal of Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectAccidenten_US
dc.subjectFirebaseen_US
dc.subjectGoogle maps APIen_US
dc.subjectTrafficen_US
dc.subjectVehicular ad hoc networksen_US
dc.titleCognitive Vehicular Network Based Accident Information System for Sustainable Trafficen_US
dc.typeArticleen_US

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