Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/6749
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dc.creatorJoão M. Moreira
dc.creatorAlípio Jorge
dc.creatorJorge Freire de Sousa
dc.creatorCarlos Soares
dc.date.accessioned2019-01-31T12:04:17Z-
dc.date.available2019-01-31T12:04:17Z-
dc.date.issued2005
dc.identifier.othersigarra:57972
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/6749-
dc.description.abstractIn 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.
dc.language.isoeng
dc.relation.ispartofAdvanced OR and AI methods in transportation
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectEngenharia
dc.subjectEngineering
dc.titleTrip time prediction in mass transit companies. A machine learning approach
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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