Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/22704
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Albuquerque, V. | - |
dc.contributor.author | Oliveira, A. | - |
dc.contributor.author | Barbosa, J. L. | - |
dc.contributor.author | Rodrigues, R. S. | - |
dc.contributor.author | Andrade, F. | - |
dc.contributor.author | Dias, J. | - |
dc.contributor.author | Ferreira, J. | - |
dc.date.accessioned | 2021-06-09T11:08:31Z | - |
dc.date.available | 2021-06-09T11:08:31Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1996-1073 | - |
dc.identifier.uri | http://hdl.handle.net/10071/22704 | - |
dc.description.abstract | Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events. | eng |
dc.language.iso | eng | - |
dc.publisher | MDPI | - |
dc.relation | UIDB/04466/2020 | - |
dc.relation | UIDP/04466/2020 | - |
dc.rights | openAccess | - |
dc.subject | Transportation | eng |
dc.subject | Traffic | eng |
dc.subject | Accidents | eng |
dc.subject | Data-driven | eng |
dc.subject | Data visualization | eng |
dc.subject | Smart cities | eng |
dc.title | Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study | eng |
dc.type | article | - |
dc.peerreviewed | yes | - |
dc.journal | Energies | - |
dc.volume | 14 | - |
dc.number | 11 | - |
degois.publication.issue | 11 | - |
degois.publication.title | Smart cities: Data-driven solutions to understand disruptive problems in transportation—The Lisbon case study | eng |
dc.date.updated | 2021-06-09T12:06:21Z | - |
dc.description.version | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.doi | 10.3390/en14113044 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.fos | Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-81836 | - |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
article_81836.pdf | Versão Editora | 919,42 kB | Adobe PDF | Ver/Abrir |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.