Factors related to highway crash severity in Brazil through a multinomial logistic regression model
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/54375 |
Resumo: | Reducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a mul.nomial logis.c regression model is fi:ed to na.onwide highway crash data in Brazil from 2017 to 2019 to iden.fy and es.mate the associated factors to crash severity. Severity is classified as without injury, with injured vic ms or with fatal vic ms. Amongst other observa.ons, results indicate that pedestrian involvement in highway crashes increase drama.cally their severity. Also, condi.ons that favor greater speeds (clear weather, .mes when there are fewer vehicles on the road) are also related to an increase in crash severity, poin.ng to a propor.onal rela.on with traffic fluidity. Moreover, some observed limita.ons on the data may indicate that Brazil would benefit greatly from na.onal crash records standards and unified databases, especially crossmatching crash reports with health data. |
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Factors related to highway crash severity in Brazil through a multinomial logistic regression modelFatores relacionados à severidade de acidentes em rodovias no Brasil através de um modelo de regressão logística multinomialRoad transportationInjury severityStatistical learningHighway crashesTraffic safetyTransporte rodoviárioSeveridade de acidentesAprendizagem estatísticaAcidentes em rodoviasSegurança viáriaReducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a mul.nomial logis.c regression model is fi:ed to na.onwide highway crash data in Brazil from 2017 to 2019 to iden.fy and es.mate the associated factors to crash severity. Severity is classified as without injury, with injured vic ms or with fatal vic ms. Amongst other observa.ons, results indicate that pedestrian involvement in highway crashes increase drama.cally their severity. Also, condi.ons that favor greater speeds (clear weather, .mes when there are fewer vehicles on the road) are also related to an increase in crash severity, poin.ng to a propor.onal rela.on with traffic fluidity. Moreover, some observed limita.ons on the data may indicate that Brazil would benefit greatly from na.onal crash records standards and unified databases, especially crossmatching crash reports with health data.Reduzir o número de mortes por acidentes de trânsito é uma prioridade importante ao redor do mundo. O estudo da severidade dos acidentes pode melhorar as políticas públicas de segurança viária, concentrando esforços nas situações associadas a acidentes de maior severidade. Neste artigo, um modelo de regressão logística multinomial é ajustado a dados de acidentes em rodovias federais no Brasil de 2017 a 2019 para estimar os fatores associados à severidade dos acidentes. A severidade é classificada como sem lesão, com vítimas feridas ou com vítimas fatais. O envolvimento de pedestres é o principal fator identificado para aumento da severidade. Além disso, condições que favorecem maiores velocidades (como tempo limpo ou horários com menos tráfego) também estão associadas com maiores severidades. Em relação ao mês, as chances de maior severidade são menores no início do ano e maiores em agosto e em novembro. As limitações observadas indicam que o Brasil carece da adoção de padrões nacionais de registro de acidentes e de bancos de dados unificados, especialmente comparando registros de acidentes rodoviários com bancos de dados de saúde.Associação Nacional de Pesquisa e Ensino em Transportes (ANPET)2022-08-29T16:51:14Z2022-08-29T16:51:14Z2022-04-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfFRANCESCHI, L. et al. Factors related to highway crash severity in Brazil through a multinomial logistic regression model. Transportes, [S.l.], v. 30, n. 1, p. 1-16, 2022. DOI: 10.14295/transportes.v30i1.2566.http://repositorio.ufla.br/jspui/handle/1/54375Transportesreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessFranceschi, LucasKaesemodel, LucianoVargas, Vera do Carmo Comparsi deKonrath, Andréa CristinaNakamura, Luiz RicardoRamires, Thiago GentilBarreto, Camila Belleza MacielValente, Amir Mattareng2023-05-19T18:50:00Zoai:localhost:1/54375Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-19T18:50Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model Fatores relacionados à severidade de acidentes em rodovias no Brasil através de um modelo de regressão logística multinomial |
title |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
spellingShingle |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model Franceschi, Lucas Road transportation Injury severity Statistical learning Highway crashes Traffic safety Transporte rodoviário Severidade de acidentes Aprendizagem estatística Acidentes em rodovias Segurança viária |
title_short |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
title_full |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
title_fullStr |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
title_full_unstemmed |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
title_sort |
Factors related to highway crash severity in Brazil through a multinomial logistic regression model |
author |
Franceschi, Lucas |
author_facet |
Franceschi, Lucas Kaesemodel, Luciano Vargas, Vera do Carmo Comparsi de Konrath, Andréa Cristina Nakamura, Luiz Ricardo Ramires, Thiago Gentil Barreto, Camila Belleza Maciel Valente, Amir Mattar |
author_role |
author |
author2 |
Kaesemodel, Luciano Vargas, Vera do Carmo Comparsi de Konrath, Andréa Cristina Nakamura, Luiz Ricardo Ramires, Thiago Gentil Barreto, Camila Belleza Maciel Valente, Amir Mattar |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Franceschi, Lucas Kaesemodel, Luciano Vargas, Vera do Carmo Comparsi de Konrath, Andréa Cristina Nakamura, Luiz Ricardo Ramires, Thiago Gentil Barreto, Camila Belleza Maciel Valente, Amir Mattar |
dc.subject.por.fl_str_mv |
Road transportation Injury severity Statistical learning Highway crashes Traffic safety Transporte rodoviário Severidade de acidentes Aprendizagem estatística Acidentes em rodovias Segurança viária |
topic |
Road transportation Injury severity Statistical learning Highway crashes Traffic safety Transporte rodoviário Severidade de acidentes Aprendizagem estatística Acidentes em rodovias Segurança viária |
description |
Reducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a mul.nomial logis.c regression model is fi:ed to na.onwide highway crash data in Brazil from 2017 to 2019 to iden.fy and es.mate the associated factors to crash severity. Severity is classified as without injury, with injured vic ms or with fatal vic ms. Amongst other observa.ons, results indicate that pedestrian involvement in highway crashes increase drama.cally their severity. Also, condi.ons that favor greater speeds (clear weather, .mes when there are fewer vehicles on the road) are also related to an increase in crash severity, poin.ng to a propor.onal rela.on with traffic fluidity. Moreover, some observed limita.ons on the data may indicate that Brazil would benefit greatly from na.onal crash records standards and unified databases, especially crossmatching crash reports with health data. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-29T16:51:14Z 2022-08-29T16:51:14Z 2022-04-07 |
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 |
FRANCESCHI, L. et al. Factors related to highway crash severity in Brazil through a multinomial logistic regression model. Transportes, [S.l.], v. 30, n. 1, p. 1-16, 2022. DOI: 10.14295/transportes.v30i1.2566. http://repositorio.ufla.br/jspui/handle/1/54375 |
identifier_str_mv |
FRANCESCHI, L. et al. Factors related to highway crash severity in Brazil through a multinomial logistic regression model. Transportes, [S.l.], v. 30, n. 1, p. 1-16, 2022. DOI: 10.14295/transportes.v30i1.2566. |
url |
http://repositorio.ufla.br/jspui/handle/1/54375 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Associação Nacional de Pesquisa e Ensino em Transportes (ANPET) |
publisher.none.fl_str_mv |
Associação Nacional de Pesquisa e Ensino em Transportes (ANPET) |
dc.source.none.fl_str_mv |
Transportes reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835135585812480 |