Identifying mobility patterns by means of centrality algorithms in multiplex networks

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dc.contributorAnálisis y Visualización de Datos en Redes (ANVIDA)es_ES
dc.contributor.authorCurado, Manuel-
dc.contributor.authorTortosa, Leandro-
dc.contributor.authorVicent, Jose F.-
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales_ES
dc.date.accessioned2021-04-28T07:37:57Z-
dc.date.available2021-04-28T07:37:57Z-
dc.date.issued2021-10-01-
dc.identifier.citationApplied Mathematics and Computation. 2021, 406: 126269. https://doi.org/10.1016/j.amc.2021.126269es_ES
dc.identifier.issn0096-3003 (Print)-
dc.identifier.issn1873-5649 (Online)-
dc.identifier.urihttp://hdl.handle.net/10045/114545-
dc.description.abstractIn this work we look for characteristics and mobility patterns in the cities of Rome and London, from a dataset of private vehicle movements in those cities. Based on mobility data and other data related to the urban public transport network, commercial activity and tourist information, a multiplex network with three layers is constructed for each city. The construction of the multiplex network allows us to establish relationships between mobility and urban bus transport system with tourism and commercial activities. From these networks, two measures of centrality in multiplex networks are calculated based on the spectral properties of a matrix constructed from the network graph and the data associated with the nodes. The centrality measures establish a ranking in the importance of the nodes within the graph. This allows us to identify the most important zones or areas within the urban layout, both from the point of view of mobility and displacement and of tourist and leisure activity within the city. Centrality mapping helps us to establish different characteristics and patterns in the car displacements in both cities.es_ES
dc.description.sponsorshipThis work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.es_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.rights© 2021 Elsevier Inc.es_ES
dc.subjectCentralityes_ES
dc.subjectMobilityes_ES
dc.subjectMultipex networkses_ES
dc.subjectAPA centralityes_ES
dc.subjectEigenvector centralityes_ES
dc.subject.otherCiencia de la Computación e Inteligencia Artificiales_ES
dc.titleIdentifying mobility patterns by means of centrality algorithms in multiplex networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.1016/j.amc.2021.126269-
dc.relation.publisherversionhttps://doi.org/10.1016/j.amc.2021.126269es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84821-P-
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