ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE

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dc.contributor.authorChuerubim, Maria Ligia
dc.contributor.authorValejo, Alan
dc.contributor.authorBezerra, Barbara Stolte [UNESP]
dc.contributor.authorSilva, Irineu da
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-10T23:58:44Z
dc.date.available2020-12-10T23:58:44Z
dc.date.issued2019-09-01
dc.description.abstractThe objective of this study is to discuss the main constraints in classifying the severity of road accidents using Artificial Neural Networks (ANN). To achieve this, ANN modelling with Multiple Layers Perceptron (MPL) was used. This method is recommended for treating non-linear problems, whose distributions are not normal, which is the case for road accidents. Variables associated with the characteristics of accidents, road infrastructure and environmental conditions were used, with the objective of identifying the influence of these factors in the accident severity. The results indicated that ANN modelling with MPL presents a potential association among the parameters related to road accidents. However, the results are limited, since the classification process provides a low rate of accuracy for accidents with victims. Such accidents correspond to less frequent observations in the database, meaning that the data is less represented, and the database becomes unbalanced. Thus, for further research studies, the use of ANN with MPL associated with data balancing methods is suggested, in order to obtain the best data fit to the model and more consistent and realistic results.en
dc.description.affiliationUniv Fed Uberlandia, Fac Civil Engn, Uberlandia, MG, Brazil
dc.description.affiliationUniv Sao Paulo, Inst Math & Comp Sci, Sao Paulo, Brazil
dc.description.affiliationUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Sch Engn Sao Carlos, Dept Transport Engn, Sao Paulo, Brazil
dc.description.affiliationUnespUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 304683/2015-9
dc.format.extent927-940
dc.identifier.citationSigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi. Istanbul: Yildiz Technical Univ, v. 37, n. 3, p. 927-940, 2019.
dc.identifier.issn1304-7205
dc.identifier.urihttp://hdl.handle.net/11449/197491
dc.identifier.wosWOS:000488302000018
dc.language.isoeng
dc.publisherYildiz Technical Univ
dc.relation.ispartofSigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi
dc.sourceWeb of Science
dc.subjectUnbalanced data
dc.subjectroad accidents
dc.subjectseverity
dc.subjectclassification
dc.subjectartificial neural networks
dc.titleARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASEen
dc.typeArtigo
dcterms.rightsHolderYildiz Technical Univ
unesp.author.lattes9897620753143611[3]
unesp.author.orcid0000-0001-5775-6683[4]
unesp.author.orcid0000-0002-8459-4664[3]

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Artigos - Engenharia Civil e Ambiental - FEB