A data mining approach for trip time prediction in mass transit companies

Detalhes bibliográficos
Autor(a) principal: João M. Moreira
Data de Publicação: 2005
Outros Autores: Alípio Jorge, Jorge Freire de Sousa, Carlos Soares
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/6750
Resumo: In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work.
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spelling A data mining approach for trip time prediction in mass transit companiesTecnologia dos transportes, Engenharia electrotécnica, electrónica e informáticaTransport technology, Electrical engineering, Electronic engineering, Information engineeringIn this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work.20052005-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/6750engJoão M. MoreiraAlípio JorgeJorge Freire de SousaCarlos Soaresinfo: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:RCAAP2023-11-29T14:29:22Zoai:repositorio-aberto.up.pt:10216/6750Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:02:22.100003Repositório 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 A data mining approach for trip time prediction in mass transit companies
title A data mining approach for trip time prediction in mass transit companies
spellingShingle A data mining approach for trip time prediction in mass transit companies
João M. Moreira
Tecnologia dos transportes, Engenharia electrotécnica, electrónica e informática
Transport technology, Electrical engineering, Electronic engineering, Information engineering
title_short A data mining approach for trip time prediction in mass transit companies
title_full A data mining approach for trip time prediction in mass transit companies
title_fullStr A data mining approach for trip time prediction in mass transit companies
title_full_unstemmed A data mining approach for trip time prediction in mass transit companies
title_sort A data mining approach for trip time prediction in mass transit companies
author João M. Moreira
author_facet João M. Moreira
Alípio Jorge
Jorge Freire de Sousa
Carlos Soares
author_role author
author2 Alípio Jorge
Jorge Freire de Sousa
Carlos Soares
author2_role author
author
author
dc.contributor.author.fl_str_mv João M. Moreira
Alípio Jorge
Jorge Freire de Sousa
Carlos Soares
dc.subject.por.fl_str_mv Tecnologia dos transportes, Engenharia electrotécnica, electrónica e informática
Transport technology, Electrical engineering, Electronic engineering, Information engineering
topic Tecnologia dos transportes, Engenharia electrotécnica, electrónica e informática
Transport technology, Electrical engineering, Electronic engineering, Information engineering
description In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/book
format book
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/6750
url https://hdl.handle.net/10216/6750
dc.language.iso.fl_str_mv eng
language eng
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.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
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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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