ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/197491 |
Resumo: | The 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. |
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Repositório Institucional da UNESP |
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ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASEUnbalanced dataroad accidentsseverityclassificationartificial neural networksThe 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Uberlandia, Fac Civil Engn, Uberlandia, MG, BrazilUniv Sao Paulo, Inst Math & Comp Sci, Sao Paulo, BrazilUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, BrazilUniv Sao Paulo, Sch Engn Sao Carlos, Dept Transport Engn, Sao Paulo, BrazilUNESP Sao Paulo State Univ, Fac Civil Engn, Sao Paulo, BrazilCNPq: 304683/2015-9Yildiz Technical UnivUniversidade Federal de Uberlândia (UFU)Universidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Chuerubim, Maria LigiaValejo, AlanBezerra, Barbara Stolte [UNESP]Silva, Irineu da2020-12-10T23:58:44Z2020-12-10T23:58:44Z2019-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article927-940Sigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi. Istanbul: Yildiz Technical Univ, v. 37, n. 3, p. 927-940, 2019.1304-7205http://hdl.handle.net/11449/197491WOS:000488302000018Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisiinfo:eu-repo/semantics/openAccess2024-06-28T12:56:41Zoai:repositorio.unesp.br:11449/197491Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:06:31.504086Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
title |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
spellingShingle |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE Chuerubim, Maria Ligia Unbalanced data road accidents severity classification artificial neural networks |
title_short |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
title_full |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
title_fullStr |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
title_full_unstemmed |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
title_sort |
ARTIFICIAL NEURAL NETWORKS RESTRICTION FOR ROAD ACCIDENTS SEVERITY CLASSIFICATION IN UNBALANCED DATABASE |
author |
Chuerubim, Maria Ligia |
author_facet |
Chuerubim, Maria Ligia Valejo, Alan Bezerra, Barbara Stolte [UNESP] Silva, Irineu da |
author_role |
author |
author2 |
Valejo, Alan Bezerra, Barbara Stolte [UNESP] Silva, Irineu da |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Uberlândia (UFU) Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Chuerubim, Maria Ligia Valejo, Alan Bezerra, Barbara Stolte [UNESP] Silva, Irineu da |
dc.subject.por.fl_str_mv |
Unbalanced data road accidents severity classification artificial neural networks |
topic |
Unbalanced data road accidents severity classification artificial neural networks |
description |
The 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-09-01 2020-12-10T23:58:44Z 2020-12-10T23:58:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Sigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi. Istanbul: Yildiz Technical Univ, v. 37, n. 3, p. 927-940, 2019. 1304-7205 http://hdl.handle.net/11449/197491 WOS:000488302000018 |
identifier_str_mv |
Sigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi. Istanbul: Yildiz Technical Univ, v. 37, n. 3, p. 927-940, 2019. 1304-7205 WOS:000488302000018 |
url |
http://hdl.handle.net/11449/197491 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sigma Journal Of Engineering And Natural Sciences-sigma Muhendislik Ve Fen Bilimleri Dergisi |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
927-940 |
dc.publisher.none.fl_str_mv |
Yildiz Technical Univ |
publisher.none.fl_str_mv |
Yildiz Technical Univ |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129020685975552 |