Business Intelligence for smart cities: Patterns and impacts of illegal parking

Detalhes bibliográficos
Autor(a) principal: Sousa, Mara Filipa Mendes
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/10362/127914
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 Business Intelligence for smart cities: Patterns and impacts of illegal parkingSmart CityLisbonParkingAbusive ParkingIllegal ParkingPredictive ModelDashboardProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe smart city concept became more than just a buzzword. Policy makers, researchers and citizens understand the necessity to improve the quality of living for its citizens using information and communication technologies (ICT). Parking has becoming an expensive resource with illegal parking raising a red flag in almost any major urban area in the world. In this context, the present work project consists on the identification of patterns and prediction of illegal and abusive on street parking in the city of Lisbon, by spatial unit and time of day. The literature review will begin with a description of Smart Cities concepts, the phenomena of urbanization and the emergence of the Illegal and abusive parking concepts within Smart Cities context. Later, it will focus on predicting illegal parking so that, evaluation metrics can be taken to be applied to the data. With these metrics, a predictive model will be developed based on the in-depth literature review of suitable studies as well as data regarding the complaints of illegal and abusive parking registered in “Na Minha Rua Lx” platform and the towed cars registered by Lisbon Municipal Police. The results will be presented on an interactive and user friendly dashboard built using advanced data analytics features of Power BI.Neto, Miguel de Castro Simões FerreiraRUNSousa, Mara Filipa Mendes2021-11-18T17:33:03Z2021-11-042021-11-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/127914TID:202839290enginfo: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:RCAAP2024-03-11T05:07:42Zoai:run.unl.pt:10362/127914Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:14.093973Repositório 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 Business Intelligence for smart cities: Patterns and impacts of illegal parking
title Business Intelligence for smart cities: Patterns and impacts of illegal parking
spellingShingle Business Intelligence for smart cities: Patterns and impacts of illegal parking
Sousa, Mara Filipa Mendes
Smart City
Lisbon
Parking
Abusive Parking
Illegal Parking
Predictive Model
Dashboard
title_short Business Intelligence for smart cities: Patterns and impacts of illegal parking
title_full Business Intelligence for smart cities: Patterns and impacts of illegal parking
title_fullStr Business Intelligence for smart cities: Patterns and impacts of illegal parking
title_full_unstemmed Business Intelligence for smart cities: Patterns and impacts of illegal parking
title_sort Business Intelligence for smart cities: Patterns and impacts of illegal parking
author Sousa, Mara Filipa Mendes
author_facet Sousa, Mara Filipa Mendes
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
RUN
dc.contributor.author.fl_str_mv Sousa, Mara Filipa Mendes
dc.subject.por.fl_str_mv Smart City
Lisbon
Parking
Abusive Parking
Illegal Parking
Predictive Model
Dashboard
topic Smart City
Lisbon
Parking
Abusive Parking
Illegal Parking
Predictive Model
Dashboard
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 2021-11-18T17:33:03Z
2021-11-04
2021-11-04T00:00:00Z
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/10362/127914
TID:202839290
url http://hdl.handle.net/10362/127914
identifier_str_mv TID:202839290
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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repository.mail.fl_str_mv
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