Factors influencing charter flight departure delay

Detalhes bibliográficos
Autor(a) principal: Fernandes, N.
Data de Publicação: 2020
Outros Autores: Moro, S., Costa, C., Aparicio, M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/20156
Resumo: This study aims to identify the main factors leading to charter flight departure delay through data mining. The data sample analysed consists of 5,484 flights operated by a European airline between 2014 and 2017. The tuned dataset of 33 features was used for modelling departure delay (e.g., if the flight delayed more than 15 minutes). The results proved the value of the proposed approach by an area under the receiver operating characteristic curve of 0.831 and supported knowledge extraction through the data-based sensitivity analysis. The features related to previous flight delay information were considered as being the most influential toward current flight being delayed or not, which is consistent with the propagating effect of flight delays. However, it is not the reason for the previous delay nor the delay duration that accounted for the most relevance. Instead, a computed feature indicating if there were two or more registered reasons accounted for 33% of relevance. The contributions include also using a broader data mining approach supported by an extensive data understanding and preparation stage using both proprietary and open access data sources to build a comprehensive dataset.
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spelling Factors influencing charter flight departure delayCharter industryFlight delayDelay predictionData miningFeature relevanceThis study aims to identify the main factors leading to charter flight departure delay through data mining. The data sample analysed consists of 5,484 flights operated by a European airline between 2014 and 2017. The tuned dataset of 33 features was used for modelling departure delay (e.g., if the flight delayed more than 15 minutes). The results proved the value of the proposed approach by an area under the receiver operating characteristic curve of 0.831 and supported knowledge extraction through the data-based sensitivity analysis. The features related to previous flight delay information were considered as being the most influential toward current flight being delayed or not, which is consistent with the propagating effect of flight delays. However, it is not the reason for the previous delay nor the delay duration that accounted for the most relevance. Instead, a computed feature indicating if there were two or more registered reasons accounted for 33% of relevance. The contributions include also using a broader data mining approach supported by an extensive data understanding and preparation stage using both proprietary and open access data sources to build a comprehensive dataset.Elsevier Science BV2022-12-09T00:00:00Z2020-01-01T00:00:00Z20202020-11-24T15:52:34Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/20156eng2210-539510.1016/j.rtbm.2019.100413Fernandes, N.Moro, S.Costa, C.Aparicio, M.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:RCAAP2023-11-09T17:27:25Zoai:repositorio.iscte-iul.pt:10071/20156Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:12:13.249599Repositório 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 Factors influencing charter flight departure delay
title Factors influencing charter flight departure delay
spellingShingle Factors influencing charter flight departure delay
Fernandes, N.
Charter industry
Flight delay
Delay prediction
Data mining
Feature relevance
title_short Factors influencing charter flight departure delay
title_full Factors influencing charter flight departure delay
title_fullStr Factors influencing charter flight departure delay
title_full_unstemmed Factors influencing charter flight departure delay
title_sort Factors influencing charter flight departure delay
author Fernandes, N.
author_facet Fernandes, N.
Moro, S.
Costa, C.
Aparicio, M.
author_role author
author2 Moro, S.
Costa, C.
Aparicio, M.
author2_role author
author
author
dc.contributor.author.fl_str_mv Fernandes, N.
Moro, S.
Costa, C.
Aparicio, M.
dc.subject.por.fl_str_mv Charter industry
Flight delay
Delay prediction
Data mining
Feature relevance
topic Charter industry
Flight delay
Delay prediction
Data mining
Feature relevance
description This study aims to identify the main factors leading to charter flight departure delay through data mining. The data sample analysed consists of 5,484 flights operated by a European airline between 2014 and 2017. The tuned dataset of 33 features was used for modelling departure delay (e.g., if the flight delayed more than 15 minutes). The results proved the value of the proposed approach by an area under the receiver operating characteristic curve of 0.831 and supported knowledge extraction through the data-based sensitivity analysis. The features related to previous flight delay information were considered as being the most influential toward current flight being delayed or not, which is consistent with the propagating effect of flight delays. However, it is not the reason for the previous delay nor the delay duration that accounted for the most relevance. Instead, a computed feature indicating if there were two or more registered reasons accounted for 33% of relevance. The contributions include also using a broader data mining approach supported by an extensive data understanding and preparation stage using both proprietary and open access data sources to build a comprehensive dataset.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01T00:00:00Z
2020
2020-11-24T15:52:34Z
2022-12-09T00:00:00Z
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/10071/20156
url http://hdl.handle.net/10071/20156
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2210-5395
10.1016/j.rtbm.2019.100413
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 Elsevier Science BV
publisher.none.fl_str_mv Elsevier Science BV
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
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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
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