Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.lnec.pt:8080/jspui/handle/123456789/1002539 |
Resumo: | Hierarchical Bayesian regression models, with differing hyper-prior distributions, are considered as accident prediction models to be fitted on data collected over several years on the Portuguese motorway network. A sensitivity analysis is performed by way of simulation to investigate the practical implications of the choice of informative hyper-priors (Gamma, Christiansen and Uniform) and non-informative Gamma, as well as various sample sizes and years of aggregated data, on the results of a road safety analysis, in particular, at detecting high accident risk locations. It was concluded that informative hyper-priors were best at detecting hotspots when small sample sizes were considered. For bigger samples the various hyper-priors produced equivalent outcomes. Furthermore, more accurate results were obtained when more years of data were analyzed. |
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Bayesian Models for the Detection of High Risk Locations on Portuguese MotorwaysBayesian analysisHierarchical regression modelsHigh accident risk locationsAccident prediction modelsHierarchical Bayesian regression models, with differing hyper-prior distributions, are considered as accident prediction models to be fitted on data collected over several years on the Portuguese motorway network. A sensitivity analysis is performed by way of simulation to investigate the practical implications of the choice of informative hyper-priors (Gamma, Christiansen and Uniform) and non-informative Gamma, as well as various sample sizes and years of aggregated data, on the results of a road safety analysis, in particular, at detecting high accident risk locations. It was concluded that informative hyper-priors were best at detecting hotspots when small sample sizes were considered. For bigger samples the various hyper-priors produced equivalent outcomes. Furthermore, more accurate results were obtained when more years of data were analyzed.Taylor & Francis Group2011-09-28T13:54:33Z2014-10-21T09:03:14Z2017-04-12T16:01:34Z2011-01-01T00:00:00Z2011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.lnec.pt:8080/jspui/handle/123456789/1002539eng978-0-415-66986-3Azeredo Lopes, S.Cardoso, J. L.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-13T03:02:47Zoai:localhost:123456789/1002539Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:38:42.431497Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
title |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
spellingShingle |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways Azeredo Lopes, S. Bayesian analysis Hierarchical regression models High accident risk locations Accident prediction models |
title_short |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
title_full |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
title_fullStr |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
title_full_unstemmed |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
title_sort |
Bayesian Models for the Detection of High Risk Locations on Portuguese Motorways |
author |
Azeredo Lopes, S. |
author_facet |
Azeredo Lopes, S. Cardoso, J. L. |
author_role |
author |
author2 |
Cardoso, J. L. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Azeredo Lopes, S. Cardoso, J. L. |
dc.subject.por.fl_str_mv |
Bayesian analysis Hierarchical regression models High accident risk locations Accident prediction models |
topic |
Bayesian analysis Hierarchical regression models High accident risk locations Accident prediction models |
description |
Hierarchical Bayesian regression models, with differing hyper-prior distributions, are considered as accident prediction models to be fitted on data collected over several years on the Portuguese motorway network. A sensitivity analysis is performed by way of simulation to investigate the practical implications of the choice of informative hyper-priors (Gamma, Christiansen and Uniform) and non-informative Gamma, as well as various sample sizes and years of aggregated data, on the results of a road safety analysis, in particular, at detecting high accident risk locations. It was concluded that informative hyper-priors were best at detecting hotspots when small sample sizes were considered. For bigger samples the various hyper-priors produced equivalent outcomes. Furthermore, more accurate results were obtained when more years of data were analyzed. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-09-28T13:54:33Z 2011-01-01T00:00:00Z 2011 2014-10-21T09:03:14Z 2017-04-12T16:01:34Z |
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 |
http://repositorio.lnec.pt:8080/jspui/handle/123456789/1002539 |
url |
http://repositorio.lnec.pt:8080/jspui/handle/123456789/1002539 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
978-0-415-66986-3 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor & Francis Group |
publisher.none.fl_str_mv |
Taylor & Francis Group |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136871597473792 |