The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

dc.authoridKatanalp, Burak Yigit/0000-0002-7172-8192
dc.contributor.authorKatanalp, Burak Yigit
dc.contributor.authorEren, Ezgi
dc.date.accessioned2025-01-06T17:38:05Z
dc.date.available2025-01-06T17:38:05Z
dc.date.issued2020
dc.description.abstractIn this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.
dc.identifier.doi10.1016/j.aap.2020.105590
dc.identifier.issn0001-4575
dc.identifier.issn1879-2057
dc.identifier.pmid32623320
dc.identifier.scopus2-s2.0-85087201781
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.aap.2020.105590
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2469
dc.identifier.volume144
dc.identifier.wosWOS:000564188000008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofAccident Analysis and Prevention
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFuzzy logic
dc.subjectDecision tree
dc.subjectInjury-severity
dc.subjectMachine learning
dc.subjectCyclist safety
dc.titleThe novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms
dc.typeArticle

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