Automatic determination of traffic accidents based on KMC-based attribute weighting

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dc.contributor.authorPolat, Kemal
dc.contributor.authorDurduran, S. Savas
dc.date.accessioned2020-03-26T18:24:04Z
dc.date.available2020-03-26T18:24:04Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9.0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53.93 and 38.76%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74.15 and 55.06% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey.en_US
dc.identifier.doi10.1007/s00521-011-0559-9en_US
dc.identifier.endpage1279en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1271en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-011-0559-9
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27781
dc.identifier.volume21en_US
dc.identifier.wosWOS:000307552600020en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_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.subjectTraffic accident analysisen_US
dc.subjectPredictionen_US
dc.subjectK-means clustering based attribute weightingen_US
dc.subjectArtificial neural networken_US
dc.subjectAdaptive network based fuzzy inference systemen_US
dc.titleAutomatic determination of traffic accidents based on KMC-based attribute weightingen_US
dc.typeArticleen_US

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