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Neural network model, based on time series, to forecast availability in the bike-shared systems

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dc.contributor.authorGutiérrez Gómez, Bosco
dc.contributor.directorAznarte Mellado, José Luis
dc.contributor.directorMedrano López, Rodrígo de
dc.date.accessioned2024-05-20T12:26:20Z
dc.date.available2024-05-20T12:26:20Z
dc.date.issued2021-09-30
dc.description.abstractThe rental of the public system of shared bicycles is a service aimed at all citizens of the city of Madrid and Barcelona, as an alternative element of clean transport that contributes to a more sustainable mobility model and the promotion of more balanced transport habits and healthy. Mobility services are increasingly based on technology and data collection, not only directly related to mobility flows, but also to other variables that affect it to a greater extent such as meteorology, pollution, strikes and temporary events. Knowing where, when and how people move is key to matching supply with demand. A better understanding of their behavior will allow us to better adapt these transport systems and optimize resources. It is necessary to predict how the system will behave to anticipate movements. Deep learning techniques have shown significant improvements in prediction over traditional models, but some difficulties and open questions remain regarding their applicability, accuracy, and ability to provide practical information. Our approach in this paper is based on comparing different models capable of predicting at least 6 hours in advance which stations are likely to be full or empty.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14248
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Inteligencia Artificial
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleNeural network model, based on time series, to forecast availability in the bike-shared systemses
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dc.typemaster thesisen
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