Identifying mobility patterns by means of centrality algorithms in multiplex networks

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Title: Identifying mobility patterns by means of centrality algorithms in multiplex networks
Authors: Curado, Manuel | Tortosa, Leandro | Vicent, Jose F.
Research Group/s: Análisis y Visualización de Datos en Redes (ANVIDA)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Centrality | Mobility | Multipex networks | APA centrality | Eigenvector centrality
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 1-Oct-2021
Publisher: Elsevier
Citation: Applied Mathematics and Computation. 2021, 406: 126269.
Abstract: In 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.
Sponsor: This work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.
ISSN: 0096-3003 (Print) | 1873-5649 (Online)
DOI: 10.1016/j.amc.2021.126269
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2021 Elsevier Inc.
Peer Review: si
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Appears in Collections:INV - ANVIDA - Artículos de Revistas

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