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dc.contributor.authorOspina Mateus, Holmanspa
dc.contributor.authorQuintana Jiménez, Leonardo Augustospa
dc.contributor.authorLópez-Valdés, Francisco Joséspa
dc.contributor.authorMorales Londoño, Nataliespa
dc.contributor.authorSalas-Navarro, Katherinnespa
dc.date.accessioned2020-02-11T15:30:17Z
dc.date.available2020-02-11T15:30:17Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11323/6023spa
dc.description.abstractObjective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low.spa
dc.language.isoeng
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectRoad crashesspa
dc.subjectPredictionspa
dc.subjectData miningspa
dc.subjectSeverityspa
dc.titleUsing Data-mining Techniques for the prediction of the severity of road crashes in Cartagena, Colombiaspa
dc.typePre-Publicaciónspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.type.coarhttp://purl.org/coar/resource_type/c_816bspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/preprintspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTOTRspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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