Trip time prediction in mass transit companies. A machine learning approach

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://repositorio-aberto.up.pt/handle/10216/6749
Resumo: In this paper we discuss how trip time prediction can be useful foroperational optimization in mass transit companies and which machine learningtechniques can be used to improve results. Firstly, we analyze which departmentsneed trip time prediction and when. Secondly, we review related work and thirdlywe present the analysis of trip time over a particular path. We proceed by presentingexperimental results conducted on real data with the forecasting techniques wefound most adequate, and conclude by discussing guidelines for future work.
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spelling Trip time prediction in mass transit companies. A machine learning approachEngenhariaEngineeringIn this paper we discuss how trip time prediction can be useful foroperational optimization in mass transit companies and which machine learningtechniques can be used to improve results. Firstly, we analyze which departmentsneed trip time prediction and when. Secondly, we review related work and thirdlywe present the analysis of trip time over a particular path. We proceed by presentingexperimental results conducted on real data with the forecasting techniques wefound most adequate, and conclude by discussing guidelines for future work.20052005-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/6749engJoão 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:RCAAP2023-11-29T15:02:29Zoai:repositorio-aberto.up.pt:10216/6749Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:14:19.079725Repositório 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 Trip time prediction in mass transit companies. A machine learning approach
title Trip time prediction in mass transit companies. A machine learning approach
spellingShingle Trip time prediction in mass transit companies. A machine learning approach
João M. Moreira
Engenharia
Engineering
title_short Trip time prediction in mass transit companies. A machine learning approach
title_full Trip time prediction in mass transit companies. A machine learning approach
title_fullStr Trip time prediction in mass transit companies. A machine learning approach
title_full_unstemmed Trip time prediction in mass transit companies. A machine learning approach
title_sort Trip time prediction in mass transit companies. A machine learning approach
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 Engenharia
Engineering
topic Engenharia
Engineering
description In this paper we discuss how trip time prediction can be useful foroperational optimization in mass transit companies and which machine learningtechniques can be used to improve results. Firstly, we analyze which departmentsneed trip time prediction and when. Secondly, we review related work and thirdlywe present the analysis of trip time over a particular path. We proceed by presentingexperimental results conducted on real data with the forecasting techniques wefound most adequate, and conclude by discussing guidelines for future work.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-01-01T00:00:00Z
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dc.type.driver.fl_str_mv info:eu-repo/semantics/book
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dc.identifier.uri.fl_str_mv https://repositorio-aberto.up.pt/handle/10216/6749
url https://repositorio-aberto.up.pt/handle/10216/6749
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
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repository.mail.fl_str_mv
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