Machine learning approaches to bike-sharing systems

Detalhes bibliográficos
Autor(a) principal: Albuquerque, Vitória
Data de Publicação: 2021
Outros Autores: Dias, Miguel Sales, Bacao, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/118827
Resumo: Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062
id RCAP_05151a04b28e07fa7740633bb82b596a
oai_identifier_str oai:run.unl.pt:10362/118827
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Machine learning approaches to bike-sharing systemsA systematic literature reviewBike-sharing systemsClassificationMachine learningPredictionPRISMA methodGeography, Planning and DevelopmentComputers in Earth SciencesEarth and Planetary Sciences (miscellaneous)SDG 11 - Sustainable Cities and CommunitiesAlbuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAlbuquerque, VitóriaDias, Miguel SalesBacao, Fernando2021-06-05T00:14:53Z2021-022021-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/118827eng2220-9964PURE: 31783673https://doi.org/10.3390/ijgi10020062info: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:RCAAP2024-03-11T05:01:35Zoai:run.unl.pt:10362/118827Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:57.429946Repositório 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
spellingShingle Machine learning approaches to bike-sharing systems
Albuquerque, Vitória
Bike-sharing systems
Classification
Machine learning
Prediction
PRISMA method
Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)
SDG 11 - Sustainable Cities and Communities
title_short Machine learning approaches to bike-sharing systems
title_full Machine learning approaches to bike-sharing systems
title_fullStr Machine learning approaches to bike-sharing systems
title_full_unstemmed Machine learning approaches to bike-sharing systems
title_sort Machine learning approaches to bike-sharing systems
author Albuquerque, Vitória
author_facet Albuquerque, Vitória
Dias, Miguel Sales
Bacao, Fernando
author_role author
author2 Dias, Miguel Sales
Bacao, Fernando
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Albuquerque, Vitória
Dias, Miguel Sales
Bacao, Fernando
dc.subject.por.fl_str_mv Bike-sharing systems
Classification
Machine learning
Prediction
PRISMA method
Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)
SDG 11 - Sustainable Cities and Communities
topic Bike-sharing systems
Classification
Machine learning
Prediction
PRISMA method
Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)
SDG 11 - Sustainable Cities and Communities
description Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062
publishDate 2021
dc.date.none.fl_str_mv 2021-06-05T00:14:53Z
2021-02
2021-02-01T00:00:00Z
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/10362/118827
url http://hdl.handle.net/10362/118827
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2220-9964
PURE: 31783673
https://doi.org/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 25
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
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
repository.mail.fl_str_mv
_version_ 1799138047955042304

Registros relacionados