Subtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case study

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dc.contributor.authorPolat, Kemal
dc.contributor.authorDurduran, S. Savas
dc.date.accessioned2020-03-26T18:16:21Z
dc.date.available2020-03-26T18:16:21Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractA case study including the discrimination of traffic accidents as accident free and accident cases on Konya-Afyonkarahisar highway in Turkey using the proposed hybrid method based on combining of a new data preprocessing method called subtractive clustering attribute weighting (SCAW) and classifier algorithms with the help of Geographical Information System (GIS) technology has been conducted. In order to improve the discrimination of classifier algorithms including artificial neural network (ANN), adaptive network based fuzzy inference system (ANFIS), support vector machine, and decision tree, using data preprocessing need in solution of these kinds of problems (traffic accident case study). So. we have proposed a novel data preprocessing method called subtractive clustering attribute weighting (SCAW) and combined with classifier algorithms. In this study, the experimental data has been obtained by means of using GIS. The obtained GIS attributes are day, temperature, humidity, weather conditions. and month of occurred accident. To evaluate the performance of the proposed hybrid method, the classification accuracy, sensitivity and specificity values have been used. The experimental obtained results are 53.93%, 52.25%, and 38.76% classification successes using alone ANN, ANFIS, and SVM with RBF kernel type, respectively. As for the proposed hybrid method, the classification accuracies of 67.98%, 70.22%, and 61.24% have been obtained using the combination of SCAW with ANN, the combination of SCAW with SVM (radial basis function (RBF) kernel type), and the combination of SCAW with ANFIS, respectively. The proposed SCAW method with the combination of classifier algorithms has been achieved the very promising results in the discrimination of traffic accidents. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.advengsoft.2011.04.001en_US
dc.identifier.endpage500en_US
dc.identifier.issn0965-9978en_US
dc.identifier.issue7en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage491en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.advengsoft.2011.04.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26853
dc.identifier.volume42en_US
dc.identifier.wosWOS:000292533000009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofADVANCES IN ENGINEERING SOFTWAREen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectGeographical Information Systems (GIS)en_US
dc.subjectAccident analysisen_US
dc.subjectSubtractive clustering attribute weighting (SCAW)en_US
dc.subjectSupport vector machineen_US
dc.subjectArtificial neural networken_US
dc.subjectAdaptive network based fuzzy inference systemen_US
dc.subjectData preprocessingen_US
dc.titleSubtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case studyen_US
dc.typeArticleen_US

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