Modeling discretionary lane-changing decisions using an improved fuzzy cognitive map with association rule mining

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dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.authoridBagdatli, Muhammed Emin Cihangir/0000-0002-1424-6920
dc.contributor.authorBagdatli, Muhammed Emin Cihangir
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:24:14Z
dc.date.available2024-11-07T13:24:14Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe discretionary lane-changing process consists of two phases. The first phase is decision making on lane-changing, and the second phase is the execution of this decision. The first phase has a complex structure that is affected by many parameters. In this phase, some parameters are present that affect lane-changing directly, while some other indirect parameters motivate drivers to perform lane-changing. This study focuses on discovering the parameters that prompt drivers to change lanes. The parameters determined as a result of the interviews with the drivers were examined in the field study. Then, the impact of the parameters for lane-changing were discovered using association rule mining and the proposed Significant Association Features Extractor (SigAFE) algorithm. Fuzzy Cognitive Map (FCM) discretionary lane-changing decision models were developed using the impact values that were discovered using the SigAFE algorithm. The performances of the models were revealed with the actual data of the field study.
dc.identifier.doi10.1080/19427867.2021.1919469
dc.identifier.endpage633
dc.identifier.issn1942-7867
dc.identifier.issn1942-7875
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85104744165
dc.identifier.scopusqualityQ2
dc.identifier.startpage623
dc.identifier.urihttps://doi.org/10.1080/19427867.2021.1919469
dc.identifier.urihttps://hdl.handle.net/11480/14000
dc.identifier.volume13
dc.identifier.wosWOS:000642525500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofTransportation Letters-The International Journal of Transportation Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectApriori algorithm
dc.subjectassociation rule mining
dc.subjectdiscretionary lane-changing
dc.subjectfuzzy cognitive map
dc.subjectlane-changing decisions
dc.titleModeling discretionary lane-changing decisions using an improved fuzzy cognitive map with association rule mining
dc.typeArticle

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