Bike-sharing mobility patterns

Detalhes bibliográficos
Autor(a) principal: Albuquerque, Vitória
Data de Publicação: 2021
Outros Autores: Andrade, Francisco, Ferreira, João Carlos, Dias, Miguel Sales, Bacao, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/126470
Resumo: Albuquerque, V., Andrade, F., Ferreira, J. C., Dias, M. S., & Bacao, F. (2021). Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities, 5(16), 1-20. [169580]. https://doi.org/10.4108/eai.4-5-2021.169580
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spelling Bike-sharing mobility patternsa data-driven analysis for the city of LisbonBike-sharing systemUrban mobility patternsStatistical analysisCluster analysisSDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesAlbuquerque, V., Andrade, F., Ferreira, J. C., Dias, M. S., & Bacao, F. (2021). Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities, 5(16), 1-20. [169580]. https://doi.org/10.4108/eai.4-5-2021.169580New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users’ demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAlbuquerque, VitóriaAndrade, FranciscoFerreira, João CarlosDias, Miguel SalesBacao, Fernando2021-10-22T03:41:10Z2021-10-132021-10-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/126470eng2518-3893PURE: 29709192https://doi.org/10.4108/eai.4-5-2021.169580info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:56:44Zoai:run.unl.pt:10362/126470Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:27:49.078921Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Bike-sharing mobility patterns
a data-driven analysis for the city of Lisbon
title Bike-sharing mobility patterns
spellingShingle Bike-sharing mobility patterns
Albuquerque, Vitória
Bike-sharing system
Urban mobility patterns
Statistical analysis
Cluster analysis
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
title_short Bike-sharing mobility patterns
title_full Bike-sharing mobility patterns
title_fullStr Bike-sharing mobility patterns
title_full_unstemmed Bike-sharing mobility patterns
title_sort Bike-sharing mobility patterns
author Albuquerque, Vitória
author_facet Albuquerque, Vitória
Andrade, Francisco
Ferreira, João Carlos
Dias, Miguel Sales
Bacao, Fernando
author_role author
author2 Andrade, Francisco
Ferreira, João Carlos
Dias, Miguel Sales
Bacao, Fernando
author2_role author
author
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
Andrade, Francisco
Ferreira, João Carlos
Dias, Miguel Sales
Bacao, Fernando
dc.subject.por.fl_str_mv Bike-sharing system
Urban mobility patterns
Statistical analysis
Cluster analysis
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
topic Bike-sharing system
Urban mobility patterns
Statistical analysis
Cluster analysis
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
description Albuquerque, V., Andrade, F., Ferreira, J. C., Dias, M. S., & Bacao, F. (2021). Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities, 5(16), 1-20. [169580]. https://doi.org/10.4108/eai.4-5-2021.169580
publishDate 2021
dc.date.none.fl_str_mv 2021-10-22T03:41:10Z
2021-10-13
2021-10-13T00:00:00Z
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/10362/126470
url http://hdl.handle.net/10362/126470
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2518-3893
PURE: 29709192
https://doi.org/10.4108/eai.4-5-2021.169580
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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