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Identifying mobility patterns by means of centrality algorithms in multiplex networks

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  • Curado, Manuel
  • Tortosa, Leandro
  • Vicent, Jose F.

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.

Suggested Citation

  • Curado, Manuel & Tortosa, Leandro & Vicent, Jose F., 2021. "Identifying mobility patterns by means of centrality algorithms in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    • Handle: RePEc:eee:apmaco:v:406:y:2021:i:c:s0096300321003581
    DOI: 10.1016/j.amc.2021.126269
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    References listed on IDEAS

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    Cited by:

    1. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Shenzhen Tian & Jialin Jiang & Hang Li & Xueming Li & Jun Yang & Chuanglin Fang, 2023. "Flow space reveals the urban network structure and development mode of cities in Liaoning, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    3. Shota Tabata, 2024. "A centrality measure for grid street network considering sequential route choice behaviour," Environment and Planning B, , vol. 51(3), pages 610-624, March.
    4. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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