Road Accident Predictions as a Classification Problem

Detalhes bibliográficos
Autor(a) principal: Agrawal, Madhulika
Data de Publicação: 2021
Outros Autores: Gonçalves, Teresa, Quaresma, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/33846
Resumo: This paper aims at evaluating the performance of various classification methods for road accident prediction. The data is collected under MO- PREVIS [3] project which aims at improving road safety in Portugal. The data is highly imbalanced as there are fewer accident instances than the non-accident ones and due to this imbalance, it is observed that the tra- ditional classification algorithms do not perform well. Using sampling techniques (undersampling and oversampling) improved the results but not significantly. Some methods resulted in increased recall but that de- creased precision as the algorithm returned more false positives to make up for data imbalance.
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spelling Road Accident Predictions as a Classification ProblemThis paper aims at evaluating the performance of various classification methods for road accident prediction. The data is collected under MO- PREVIS [3] project which aims at improving road safety in Portugal. The data is highly imbalanced as there are fewer accident instances than the non-accident ones and due to this imbalance, it is observed that the tra- ditional classification algorithms do not perform well. Using sampling techniques (undersampling and oversampling) improved the results but not significantly. Some methods resulted in increased recall but that de- creased precision as the algorithm returned more false positives to make up for data imbalance.2023-02-03T12:10:03Z2023-02-032021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/33846http://hdl.handle.net/10174/33846engMadhulika Agrawal, Teresa Gonçalves, and Paulo Quaresma. Road Accident Predictions as a Classification Problem. In Proceedings of the 27th Portuguese Conference on Pattern Recognition, RECPAD 2021, 2021.ndtcg@uevora.ptnd283Agrawal, MadhulikaGonçalves, TeresaQuaresma, Pauloinfo: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:RCAAP2024-01-03T19:35:57Zoai:dspace.uevora.pt:10174/33846Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:22:35.225492Repositório 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 Road Accident Predictions as a Classification Problem
title Road Accident Predictions as a Classification Problem
spellingShingle Road Accident Predictions as a Classification Problem
Agrawal, Madhulika
title_short Road Accident Predictions as a Classification Problem
title_full Road Accident Predictions as a Classification Problem
title_fullStr Road Accident Predictions as a Classification Problem
title_full_unstemmed Road Accident Predictions as a Classification Problem
title_sort Road Accident Predictions as a Classification Problem
author Agrawal, Madhulika
author_facet Agrawal, Madhulika
Gonçalves, Teresa
Quaresma, Paulo
author_role author
author2 Gonçalves, Teresa
Quaresma, Paulo
author2_role author
author
dc.contributor.author.fl_str_mv Agrawal, Madhulika
Gonçalves, Teresa
Quaresma, Paulo
description This paper aims at evaluating the performance of various classification methods for road accident prediction. The data is collected under MO- PREVIS [3] project which aims at improving road safety in Portugal. The data is highly imbalanced as there are fewer accident instances than the non-accident ones and due to this imbalance, it is observed that the tra- ditional classification algorithms do not perform well. Using sampling techniques (undersampling and oversampling) improved the results but not significantly. Some methods resulted in increased recall but that de- creased precision as the algorithm returned more false positives to make up for data imbalance.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01T00:00:00Z
2023-02-03T12:10:03Z
2023-02-03
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://hdl.handle.net/10174/33846
http://hdl.handle.net/10174/33846
url http://hdl.handle.net/10174/33846
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Madhulika Agrawal, Teresa Gonçalves, and Paulo Quaresma. Road Accident Predictions as a Classification Problem. In Proceedings of the 27th Portuguese Conference on Pattern Recognition, RECPAD 2021, 2021.
nd
tcg@uevora.pt
nd
283
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