Traffic accident severity prediction with ensemble learning methods

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dc.authoridCEVEN, SULEYMAN/0000-0002-8970-4826
dc.authoridALBAYRAK, AHMET/0000-0002-2166-1102
dc.contributor.authorCeven, Sueleyman
dc.contributor.authorAlbayrak, Ahmet
dc.date.accessioned2024-09-29T15:55:14Z
dc.date.available2024-09-29T15:55:14Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, decision tree-based models are proposed for classification of traffic accident severity. Traffic accident severity is classified into three categories. The data set used in the study belongs to the province of Kayseri, Turkey. The data consists of urban traffic accident reports (23074 accidents) between 2013 and 2021. There are 39 variables in the data set. As a result of data preprocessing, 15 variables that are meaningful and can be used for the model in the data set were determined. Since the input variables of the model mainly contain categorical data, they were coded with pseudo-coding and a total of 93 input variables were obtained. In the studies, ensemble learning methods such as Random Forest, AdaBoost and MLP methods were used. F1 scores of these methods were found to be 91.72%, 91.27% and 88.95%, respectively. Feature importance levels were calculated for 15 variables used in the model. Gini index and decision trees were used while calculating the importance of the features. Driver fault (0.64) was found to have the most effect on traffic accident severity. This study focuses especially on urban traffic accidents. Urban traffic is crowded in terms of both vehicles and pedestrians. As a result of this, according to the findings obtained in this study, traffic accidents occurred mostly at the intersections with crowded urban areas.en_US
dc.description.sponsorshipScientific Research Projects (BAP) Coordinatorship of Karabuk University [KBBAP-22-DR-015]en_US
dc.description.sponsorshipWe would like to thank Kayseri Governorship, Kayseri Police Department and Regional Traffic Inspection Branch for their support in obtaining traffic accident data. This study was financed by the Scientific Research Projects (BAP) Coordinatorship of Karabuk University with the project number KBUEBAP-22-DR-015.en_US
dc.identifier.doi10.1016/j.compeleceng.2024.109101
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85183581952en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2024.109101
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4516
dc.identifier.volume114en_US
dc.identifier.wosWOS:001171631200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTraffic accident severityen_US
dc.subjectEnsemble learningen_US
dc.subjectRandom Foresten_US
dc.subjectAdaBoosten_US
dc.subjectFeature importanceen_US
dc.titleTraffic accident severity prediction with ensemble learning methodsen_US
dc.typeArticleen_US

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