Machine learning approaches to bike-sharing systems: A systematic literature review
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/22120 |
Resumo: | Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction. |
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Machine learning approaches to bike-sharing systems: A systematic literature reviewBike-sharing systemsMachine learningClassificationPredictionPRISMA methodCities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.MDPI2021-02-22T15:55:08Z2021-01-01T00:00:00Z20212021-02-22T15:53:39Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/22120eng2220-996410.3390/ijgi10020062Albuquerque, V.Dias, J.Bacao, F.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:RCAAP2023-11-09T18:02:27Zoai:repositorio.iscte-iul.pt:10071/22120Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:33:42.279289Repositório 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 |
Machine learning approaches to bike-sharing systems: A systematic literature review |
title |
Machine learning approaches to bike-sharing systems: A systematic literature review |
spellingShingle |
Machine learning approaches to bike-sharing systems: A systematic literature review Albuquerque, V. Bike-sharing systems Machine learning Classification Prediction PRISMA method |
title_short |
Machine learning approaches to bike-sharing systems: A systematic literature review |
title_full |
Machine learning approaches to bike-sharing systems: A systematic literature review |
title_fullStr |
Machine learning approaches to bike-sharing systems: A systematic literature review |
title_full_unstemmed |
Machine learning approaches to bike-sharing systems: A systematic literature review |
title_sort |
Machine learning approaches to bike-sharing systems: A systematic literature review |
author |
Albuquerque, V. |
author_facet |
Albuquerque, V. Dias, J. Bacao, F. |
author_role |
author |
author2 |
Dias, J. Bacao, F. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Albuquerque, V. Dias, J. Bacao, F. |
dc.subject.por.fl_str_mv |
Bike-sharing systems Machine learning Classification Prediction PRISMA method |
topic |
Bike-sharing systems Machine learning Classification Prediction PRISMA method |
description |
Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-22T15:55:08Z 2021-01-01T00:00:00Z 2021 2021-02-22T15:53:39Z |
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/10071/22120 |
url |
http://hdl.handle.net/10071/22120 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2220-9964 10.3390/ijgi10020062 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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