Publicação
Cities Mobility Management: Mobility prediction model applied to Lisbon marathons
| dc.contributor.advisor | Neto, Miguel de Castro Simões Ferreira | |
| dc.contributor.advisor | Sarmento, Pedro Alexandre Reis | |
| dc.contributor.advisor | Nascimento, Marcel Motta do | |
| dc.contributor.author | Domingues, Fábio Miguel | |
| dc.date.accessioned | 2021-09-10T09:44:31Z | |
| dc.date.available | 2021-09-10T09:44:31Z | |
| dc.date.issued | 2021-07-20 | |
| dc.description | Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | Lisbon, as Portugal capital, is a city that receives a vast number of events every year. These enormous concentrations of people, in the same place, at the same time, requires huge transport services planning and organization. To elevate Lisbon to a state of what is called smart city, smart mobility topic must be improved, in a serious way. To achieve this, Lisbon must be able to stop being reactive and start being proactive. Predicting people’s behavior, concerning to the theme “mobility” is something that can improve drastically population’s life quality. With this study, it is intended to have a better and specific understanding of how CARRIS public transport service is managed in Lisbon, during city’s marathons. The main objective is to implement smarter mobility strategies, during big events, analyze people behavior before, during and after these events and try to predict future population behavior, based on data. CARRIS and other related sources provided data that was integrated into a database system in an automated way. Above this database system a machine learning prediction model was put in place revealing that it is possible to forecast at least 75% of the attendance on this type of transports. This project will end with Power BI self-explanatory reports that can help decision making. By having a better understanding of all problems related to smart mobility during big events, Lisbon may predict and act accordingly. By improving smart mobility, the city is improving life quality, as well. | pt_PT |
| dc.identifier.tid | 202763579 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/124338 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Smart mobility | pt_PT |
| dc.subject | Smart Cities | pt_PT |
| dc.subject | Smart event planning | pt_PT |
| dc.subject | Citizen’s life quality | pt_PT |
| dc.subject | Massive mobility | pt_PT |
| dc.title | Cities Mobility Management: Mobility prediction model applied to Lisbon marathons | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio (Business Intelligence) | pt_PT |
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