Station segmentation of Lisbon bicycle sharing system based on users demand and supply

Detalhes bibliográficos
Autor(a) principal: Fernandes, Marisa Martinho
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/120569
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Station segmentation of Lisbon bicycle sharing system based on users demand and supplyMachine learningTimeseries segmentationBike-sharing systemsSustainable mobilityProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBike-sharing systems are well known in the sustainable mobility field and have several aspects that need optimization and improvement. One of the most relevant aspects is station segmentation based on user demand and supply, and it is the focus of the thesis. The segmentation work has an enormous potential to reduce complexity in predicting the bicycle demand and supply, thus improving the overall quality of service. Several machine learning algorithms were used to investigate the aforementioned segmentation task. This work considers two popular and well-known clustering algorithms to extract and analyze interesting patterns, like the difference between arrivals and departures throughout time and stations: the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the hierarchical clustering. The algorithms are applied to the specific case of GIRA, the bicycle sharing system (BSS) of the city of Lisbon. The obtained results suggest that considering the variables under analysis, the optimal number of clusters to be used in a second phase of the BSS optimization (demand and supply forecast) is the same as the number of stations in the Lisbon BSS. The results are very insightful and allow future work to focus either on the demand forecast or the enrichment of the variables under study.Castelli, MauroRUNFernandes, Marisa Martinho2021-07-06T11:08:43Z2021-05-242021-05-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/120569TID:202731669enginfo: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:RCAAP2024-03-11T05:03:04Zoai:run.unl.pt:10362/120569Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:23.510753Repositório 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 Station segmentation of Lisbon bicycle sharing system based on users demand and supply
title Station segmentation of Lisbon bicycle sharing system based on users demand and supply
spellingShingle Station segmentation of Lisbon bicycle sharing system based on users demand and supply
Fernandes, Marisa Martinho
Machine learning
Timeseries segmentation
Bike-sharing systems
Sustainable mobility
title_short Station segmentation of Lisbon bicycle sharing system based on users demand and supply
title_full Station segmentation of Lisbon bicycle sharing system based on users demand and supply
title_fullStr Station segmentation of Lisbon bicycle sharing system based on users demand and supply
title_full_unstemmed Station segmentation of Lisbon bicycle sharing system based on users demand and supply
title_sort Station segmentation of Lisbon bicycle sharing system based on users demand and supply
author Fernandes, Marisa Martinho
author_facet Fernandes, Marisa Martinho
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Fernandes, Marisa Martinho
dc.subject.por.fl_str_mv Machine learning
Timeseries segmentation
Bike-sharing systems
Sustainable mobility
topic Machine learning
Timeseries segmentation
Bike-sharing systems
Sustainable mobility
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2021
dc.date.none.fl_str_mv 2021-07-06T11:08:43Z
2021-05-24
2021-05-24T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/120569
TID:202731669
url http://hdl.handle.net/10362/120569
identifier_str_mv TID:202731669
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)
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
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