Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System

Bibliographic Details
Main Author: Ribeiro, Margarida Maria Garcia Sequeira
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/174370
Summary: Dissertation 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 Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing SystemMicromobilityTime Series ClusteringSpatial Temporal AnalysisDocked Bike Sharing SystemSDG 3 - Good health and well-beingSDG 11 - Sustainable cities and communitiesSDG 13 - Climate actionDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBike sharing systems (BSS) are a crucial component of sustainable urban mobility. With urbanization on the rise, these systems present a viable solution to reduce the carbon footprint of the transportation sector and combat environmental impact. However, for BSS to be effective, they must operate efficiently; otherwise, users are less likely to adopt them. Understanding the patterns and trends of GIRA – Bicicletas de Lisboa, Lisbon’s public docked BSS, is essential for maintaining proper operation and ensuring Lisbon's investment in BSS to promote a more sustainable city. Despite the popularity of BSS, a significant research gap remains concerning Lisbon's BSS, particularly in understanding dock occupancy rates and seasonal fluctuations. This study's primary aim is to identify and analyse distinct occupancy patterns of bike stations throughout the different seasons of the year and understand their geographic context. The study uses time series to capture occupancy patterns over time for stations, grouping them using three clustering algorithms: Hierarchical Clustering, K-Means, and DBSCAN. Dynamic Time Warping (DTW) is employed to compare the time series data, and DTW Barycentre Averaging is used to visualize the final time series for each cluster. A geographic distribution analysis of stations and an assessment of nearby points of interest were also conducted. The findings reveal two main types of stations: commercial stations and residential stations, each exhibiting unique occupancy patterns consistent across all seasons. Commercial stations are centrally located and dispersed throughout Lisbon, whereas residential stations are more linear and concentrated near the river. Both types frequently appear near gardens, parks, and educational facilities, with residential stations often located near schools. These results reflect typical urban movement patterns, showing that GIRA effectively meets Lisbon residents' transportation needs for daily commutes and routine activities, despite challenges in maintaining bike availability.Neto, Miguel de Castro Simões FerreiraAlpalhão, Nuno Tiago FalcãoRUNRibeiro, Margarida Maria Garcia Sequeira2024-10-282025-10-28T00:00:00Z2024-10-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/174370TID:203778030enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-01-13T01:40:52Zoai:run.unl.pt:10362/174370Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:12:43.976688Repositórios 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 Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
title Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
spellingShingle Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
Ribeiro, Margarida Maria Garcia Sequeira
Micromobility
Time Series Clustering
Spatial Temporal Analysis
Docked Bike Sharing System
SDG 3 - Good health and well-being
SDG 11 - Sustainable cities and communities
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
title_full Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
title_fullStr Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
title_full_unstemmed Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
title_sort Understanding Occupation Patterns in Lisbon’s Docked Bike Sharing System
author Ribeiro, Margarida Maria Garcia Sequeira
author_facet Ribeiro, Margarida Maria Garcia Sequeira
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
Alpalhão, Nuno Tiago Falcão
RUN
dc.contributor.author.fl_str_mv Ribeiro, Margarida Maria Garcia Sequeira
dc.subject.por.fl_str_mv Micromobility
Time Series Clustering
Spatial Temporal Analysis
Docked Bike Sharing System
SDG 3 - Good health and well-being
SDG 11 - Sustainable cities and communities
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Micromobility
Time Series Clustering
Spatial Temporal Analysis
Docked Bike Sharing System
SDG 3 - Good health and well-being
SDG 11 - Sustainable cities and communities
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2024
dc.date.none.fl_str_mv 2024-10-28
2024-10-28T00:00:00Z
2025-10-28T00:00:00Z
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/10362/174370
TID:203778030
url http://hdl.handle.net/10362/174370
identifier_str_mv TID:203778030
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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