Decision support service for Bewegen bike-sharing systems
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/29670 |
Resumo: | Bike-sharing systems (BSS) are becoming very popular and, consequently, their management is becoming more complex. The main objective of this dissertation is the development of a decision support service for Bewegen bike-sharing systems applying machine learning (ML) methods. An additional objective is the development of an appropriate mechanism for systematic data collection, required in the development and test of the ML methods. The decision support service has two goals. The first goal is the prediction of the number of bikes in each station 30 minutes ahead of time, to be provided to the bike-sharing system clients. The second goal is the prediction of the number of bikes in each station 24 hours ahead of time, to be provided to the bike-sharing operators when deciding how to redistribute bikes among the different stations. In order to reach these two goals, two ML approaches were implemented: a neural network (NN) model and a k-nearest neighbour (k-NN) algorithm. The tests have shown that the NN algorithms provide better prediction results on both goals. The prediction algorithms were trained and tested with collected historical data from one of the Bewegen's BSS from 1 of January, 2019 until 30 of April, 2019. |
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Decision support service for Bewegen bike-sharing systemsBike-sharingMachine learningFeature selectionFeature visualisationNeural networksk-nearest neighbourBike-sharing systems (BSS) are becoming very popular and, consequently, their management is becoming more complex. The main objective of this dissertation is the development of a decision support service for Bewegen bike-sharing systems applying machine learning (ML) methods. An additional objective is the development of an appropriate mechanism for systematic data collection, required in the development and test of the ML methods. The decision support service has two goals. The first goal is the prediction of the number of bikes in each station 30 minutes ahead of time, to be provided to the bike-sharing system clients. The second goal is the prediction of the number of bikes in each station 24 hours ahead of time, to be provided to the bike-sharing operators when deciding how to redistribute bikes among the different stations. In order to reach these two goals, two ML approaches were implemented: a neural network (NN) model and a k-nearest neighbour (k-NN) algorithm. The tests have shown that the NN algorithms provide better prediction results on both goals. The prediction algorithms were trained and tested with collected historical data from one of the Bewegen's BSS from 1 of January, 2019 until 30 of April, 2019.Os sistemas de bike-sharing estão a tornar-se cada vez mais populares e a sua gestão mais complexa. O objetivo principal desta dissertação é o desenvolvimento de um serviço de suporte de decisão, baseado em métodos de aprendizagem automática, para os sistemas de bikesharing da empresa Bewegen. Um objetivo secundário é o desenvolvimento de um mecanismo de recolha sistemática de dados de utilização do sistema, necessários ao desenvolvimento e teste dos métodos de aprendizagem automática. O serviço de suporte de decisão tem dois objetivos. O primeiro objetivo é a previsão do número de bicicletas em cada estação com 30 minutos de antecedência, informação esta a disponilizar aos clientes do sistema de bike-sharing. O segundo objetivo é a previsão do número de bicicletas em cada estacão com 24 horas de antecedência, informação esta a disponilizar aos operadores do sistema no planeamento da distribuição das bicicletas pelas diferentes estacões. Para cumprir com estes objetivos foram implementados dois algoritmos de aprendizagem automática: uma rede neuronal e um algoritmo k-nearest neighbour. Os testes realizados mostram que os algoritmos baseados em redes neuronais obtém melhor desempenho nos dois objectivos. Os dados utilizados nos testes dos dois algoritmos são os dados históricos de um dos sistemas da Bewegen recolhidos desde 1 de janeiro de 2019 até 30 de abril de 2019.2019-072019-07-01T00:00:00Z2021-07-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/29670engSousa, Diogo Macedo deinfo: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:RCAAP2024-02-22T11:57:25Zoai:ria.ua.pt:10773/29670Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:56.954655Repositório 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 |
Decision support service for Bewegen bike-sharing systems |
title |
Decision support service for Bewegen bike-sharing systems |
spellingShingle |
Decision support service for Bewegen bike-sharing systems Sousa, Diogo Macedo de Bike-sharing Machine learning Feature selection Feature visualisation Neural networks k-nearest neighbour |
title_short |
Decision support service for Bewegen bike-sharing systems |
title_full |
Decision support service for Bewegen bike-sharing systems |
title_fullStr |
Decision support service for Bewegen bike-sharing systems |
title_full_unstemmed |
Decision support service for Bewegen bike-sharing systems |
title_sort |
Decision support service for Bewegen bike-sharing systems |
author |
Sousa, Diogo Macedo de |
author_facet |
Sousa, Diogo Macedo de |
author_role |
author |
dc.contributor.author.fl_str_mv |
Sousa, Diogo Macedo de |
dc.subject.por.fl_str_mv |
Bike-sharing Machine learning Feature selection Feature visualisation Neural networks k-nearest neighbour |
topic |
Bike-sharing Machine learning Feature selection Feature visualisation Neural networks k-nearest neighbour |
description |
Bike-sharing systems (BSS) are becoming very popular and, consequently, their management is becoming more complex. The main objective of this dissertation is the development of a decision support service for Bewegen bike-sharing systems applying machine learning (ML) methods. An additional objective is the development of an appropriate mechanism for systematic data collection, required in the development and test of the ML methods. The decision support service has two goals. The first goal is the prediction of the number of bikes in each station 30 minutes ahead of time, to be provided to the bike-sharing system clients. The second goal is the prediction of the number of bikes in each station 24 hours ahead of time, to be provided to the bike-sharing operators when deciding how to redistribute bikes among the different stations. In order to reach these two goals, two ML approaches were implemented: a neural network (NN) model and a k-nearest neighbour (k-NN) algorithm. The tests have shown that the NN algorithms provide better prediction results on both goals. The prediction algorithms were trained and tested with collected historical data from one of the Bewegen's BSS from 1 of January, 2019 until 30 of April, 2019. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07 2019-07-01T00:00:00Z 2021-07-29T00:00:00Z |
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/10773/29670 |
url |
http://hdl.handle.net/10773/29670 |
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 |
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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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1799137675316297728 |