IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v406y2021ics0096300321003581.html
   My bibliography  Save this article

Identifying mobility patterns by means of centrality algorithms in multiplex networks

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321003581
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2021.126269?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    2. Meead Saberi & Hani S. Mahmassani & Dirk Brockmann & Amir Hosseini, 2017. "A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks," Transportation, Springer, vol. 44(6), pages 1383-1402, November.
    3. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
    4. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    5. Yao Shen & Kayvan Karimi, 2018. "Urban evolution as a spatio-functional interaction process: the case of central Shanghai," Journal of Urban Design, Taylor & Francis Journals, vol. 23(1), pages 42-70, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    2. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    3. Keng, Ying Ying & Kwa, Kiam Heong & Ratnavelu, Kurunathan, 2021. "Centrality analysis in a drug network and its application to drug repositioning," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    4. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    5. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    6. Teqi Dai & Tiantian Ding & Qingfang Liu & Bingxin Liu, 2022. "Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    7. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    8. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    9. Federico Karagulian & Gaetano Valenti & Carlo Liberto & Matteo Corazza, 2022. "A Methodology to Estimate Functional Vulnerability Using Floating Car Data," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    10. Wang, Weiping & Guo, Junjiang & Wang, Zhen & Wang, Hao & Cheng, Jun & Wang, Chunyang & Yuan, Manman & Kurths, Jürgen & Luo, Xiong & Gao, Yang, 2021. "Abnormal flow detection in industrial control network based on deep reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    11. Giacopelli, G. & Migliore, M. & Tegolo, D., 2020. "Graph-theoretical derivation of brain structural connectivity," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    12. da Cunha, Éverton Fernandes & da Fontoura Costa, Luciano, 2022. "On hypercomplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    13. Mirco Nanni & Leandro Tortosa & José F Vicent & Gevorg Yeghikyan, 2020. "Ranking places in attributed temporal urban mobility networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-25, October.
    14. Zhu, Linhe & Liu, Mengxue & Li, Yimin, 2019. "The dynamics analysis of a rumor propagation model in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 118-137.
    15. Wei, Sheng & Zheng, Wei & Wang, Lei, 2021. "Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect," Transport Policy, Elsevier, vol. 104(C), pages 1-17.
    16. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    17. Zachary Neal, 2018. "Is the Urban World Small? The Evidence for Small World Structure in Urban Networks," Networks and Spatial Economics, Springer, vol. 18(3), pages 615-631, September.
    18. Ding Ma & Itzhak Omer & Toshihiro Osaragi & Mats Sandberg & Bin Jiang, 2019. "Why topology matters in predicting human activities," Environment and Planning B, , vol. 46(7), pages 1297-1313, September.
    19. Liu, Wanping & Wu, Xiao & Yang, Wu & Zhu, Xiaofei & Zhong, Shouming, 2019. "Modeling cyber rumor spreading over mobile social networks: A compartment approach," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 214-229.
    20. Bodaghi, Amirhosein & Goliaei, Sama & Salehi, Mostafa, 2019. "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 167-184.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:406:y:2021:i:c:s0096300321003581. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.