On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach

Detalhes bibliográficos
Autor(a) principal: Luís Moreira Matias
Data de Publicação: 2014
Outros Autores: João Mendes Moreira, João Gama, Michel Ferreira
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/83023
Resumo: Nowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising.
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spelling On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning ApproachInteligência artificial, Engenharia electrotécnica, electrónica e informáticaArtificial intelligence, Electrical engineering, Electronic engineering, Information engineeringNowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising.20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/83023engLuís Moreira MatiasJoão Mendes MoreiraJoão GamaMichel Ferreirainfo: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:RCAAP2023-11-29T14:03:14Zoai:repositorio-aberto.up.pt:10216/83023Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:53:34.277638Repositório 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 On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
title On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
spellingShingle On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
Luís Moreira Matias
Inteligência artificial, Engenharia electrotécnica, electrónica e informática
Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering
title_short On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
title_full On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
title_fullStr On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
title_full_unstemmed On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
title_sort On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
author Luís Moreira Matias
author_facet Luís Moreira Matias
João Mendes Moreira
João Gama
Michel Ferreira
author_role author
author2 João Mendes Moreira
João Gama
Michel Ferreira
author2_role author
author
author
dc.contributor.author.fl_str_mv Luís Moreira Matias
João Mendes Moreira
João Gama
Michel Ferreira
dc.subject.por.fl_str_mv Inteligência artificial, Engenharia electrotécnica, electrónica e informática
Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering
topic Inteligência artificial, Engenharia electrotécnica, electrónica e informática
Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering
description Nowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01T00:00:00Z
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/10216/83023
url https://hdl.handle.net/10216/83023
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)
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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