Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/31711
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Campo DCValorIdioma
dc.contributor.authorLeal, D.-
dc.contributor.authorAlbuquerque, V.-
dc.contributor.authorDias, J.-
dc.contributor.authorFerreira, J.-
dc.contributor.editorAna Lucia Martins-
dc.contributor.editorJoao C. Ferreira-
dc.contributor.editorAlexander Kocian-
dc.contributor.editorUlpan Tokkozhina-
dc.date.accessioned2024-05-16T08:27:25Z-
dc.date.available2024-05-16T08:27:25Z-
dc.date.issued2023-
dc.identifier.citationLeal, D., Albuquerque, V., Dias, J., & Ferreira, J. (2023) Analyzing urban mobility based on smartphone data: The Lisbon case study. In A. L. Martins, J. C. Ferreira, A. Kocian, & U. Tokkozhina (Eds.). 6th EAI International Conference on Intelligent Transport Systems, INTSYS 2022, Proceedings (pp. 40-54). Springer. https://doi.org/10.1007/978-3-031-30855-0_3-
dc.identifier.isbn978-3-031-30855-0-
dc.identifier.urihttp://hdl.handle.net/10071/31711-
dc.description.abstractOur paper addresses the mobility patterns in Lisbon in the vicinity of historical and transportation points of interest, with a case study conducted in the parish of Santa Maria Maior, a vibrant touristic neighborhood. We propose a data science-based approach to analyze such patterns. Our dataset includes five months of georeferenced mobile phone data, collected during late 2021 and early 2022, provided by the municipality of Lisbon. We performed a systematic literature review, using the PRISMA methodology and adopted the CRISP-DM methodology, to perform data curation, statistical and clustering analysis, and visualization, following the recommendations of the literature. For clustering we used the DBSCAN algorithm. We found eight clusters in Santa Maria Maior, with outstanding clusters along 28-E tram and Lisbon Cruise Terminal, where mobility is high, particularly for non-roaming travelers. This paper contributes to the digital transformation of Lisbon into a smart city, by improving improved understanding of urban mobility patternseng
dc.language.isoeng-
dc.publisherSpringer, Cham-
dc.relation.ispartof6th EAI International Conference on Intelligent Transport Systems, INTSYS 2022, Proceedings-
dc.rightsopenAccess-
dc.subjectCRISP-DMeng
dc.subjectDBSCANeng
dc.subjectPoint of interesteng
dc.subjectPRISMA Programa Comunitário Relativo à Preparação das Empresas com Vista ao Mercado Únicoeng
dc.subjectSmartphone dataeng
dc.subjectUrban mobilityeng
dc.subjectVisualisationeng
dc.titleAnalyzing urban mobility based on smartphone data: The Lisbon case studyeng
dc.typeconferenceObject-
dc.event.typeConferênciapt
dc.event.locationLisboaeng
dc.event.date2023-
dc.pagination40 - 54-
dc.peerreviewedyes-
dc.date.updated2024-05-16T09:26:23Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1007/978-3-031-30855-0_3-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.fosDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
iscte.subject.odsCidades e comunidades sustentáveispor
iscte.subject.odsAção climáticapor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-96208-
iscte.alternateIdentifiers.scopus2-s2.0-85161597239-
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