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Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study

Bibliographic Details
Main Author: Oliveira, Rita Madalena Cardoso da Silva
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/175416
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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network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case StudyBike-sharingSoft MobilityMachine LearningPredictive ModelingCyclingSDG 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 IntelligenceThe Internet of Things (IoT) uses intelligent technology and interconnected devices to gather real-time data, enabling a shift to sustainable transportation modes. This has made bikesharing systems (BSS) a popular and reliable form of soft mobility in urban environments. This study leverages these technological advancements to understand how city events affect Lisbon's BSS, GIRA, by predicting hourly station occupancy rates using the CRISP-DM methodology. By integrating weather information and event data into BSS station-level data spanning the year of 2022, and applying state-of-art machine learning (ML) algorithms – such as Random Forest (RF), Gradient Boosting Tree (GBT), and Extreme Gradient Boosting (XGBoost) – the research aims to optimize station occupancy management during events. This contributes to more efficient urban transportation systems and a sustainable future for Lisbon. Major findings show that XGBoost outperformed the other algorithms, having a higher predictive accuracy during sport event days, and in music event days stations near Coliseu dos Recreios require strong rebalancing efforts.Jardim, João Bruno Morais de SousaAlbuquerque, Lídia Vitória Pires deRUNOliveira, Rita Madalena Cardoso da Silva2024-11-082025-11-08T00:00:00Z2024-11-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/175416TID:203777999enginfo: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:RCAAP2025-01-13T01:43:39Zoai:run.unl.pt:10362/175416Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:15:49.186142Repositórios 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 Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
title Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
spellingShingle Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
Oliveira, Rita Madalena Cardoso da Silva
Bike-sharing
Soft Mobility
Machine Learning
Predictive Modeling
Cycling
SDG 11 - Sustainable cities and communities
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
title_full Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
title_fullStr Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
title_full_unstemmed Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
title_sort Predicting Station Occupancy on Bike-Sharing System During Events: The Lisbon Case Study
author Oliveira, Rita Madalena Cardoso da Silva
author_facet Oliveira, Rita Madalena Cardoso da Silva
author_role author
dc.contributor.none.fl_str_mv Jardim, João Bruno Morais de Sousa
Albuquerque, Lídia Vitória Pires de
RUN
dc.contributor.author.fl_str_mv Oliveira, Rita Madalena Cardoso da Silva
dc.subject.por.fl_str_mv Bike-sharing
Soft Mobility
Machine Learning
Predictive Modeling
Cycling
SDG 11 - Sustainable cities and communities
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Bike-sharing
Soft Mobility
Machine Learning
Predictive Modeling
Cycling
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 2024-11-08
2024-11-08T00:00:00Z
2025-11-08T00:00:00Z
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/10362/175416
TID:203777999
url http://hdl.handle.net/10362/175416
identifier_str_mv TID:203777999
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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