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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorChuerubim, Maria Ligia-
Autor(es): dc.creatorValejo, Alan-
Autor(es): dc.creatorBezerra, Barbara Stolte [UNESP]-
Autor(es): dc.creatorSilva, Irineu da-
Data de aceite: dc.date.accessioned2022-02-22T00:13:18Z-
Data de disponibilização: dc.date.available2022-02-22T00:13:18Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-09-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/197491-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/197491-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUniv Fed Uberlandia, Fac Civil Engn, Uberlandia, MG, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Inst Math & Comp Sci, Sao Paulo, Brazil-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Sch Engn Sao Carlos, Dept Transport Engn, Sao Paulo, Brazil-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, Brazil-
Descrição: dc.descriptionCNPq: 304683/2015-9-
Formato: dc.format927-940-
Idioma: dc.languageen-
Publicador: dc.publisherYildiz Technical Univ-
Relação: dc.relationSigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectUnbalanced data-
Palavras-chave: dc.subjectroad accidents-
Palavras-chave: dc.subjectseverity-
Palavras-chave: dc.subjectclassification-
Palavras-chave: dc.subjectartificial neural networks-
Título: dc.titleARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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