IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v347y2019icp600-615.html
   My bibliography  Save this article

A variant of the current flow betweenness centrality and its application in urban networks

Author

Listed:
  • Agryzkov, Taras
  • Tortosa, Leandro
  • Vicent, Jose F.

Abstract

The current flow betweenness centrality is a useful tool to estimate traffic status in spatial networks and, in general, to measure the intermediation of nodes in networks where the transition between them takes place in a random way. The main drawback of this centrality is its high computational cost, especially for very large networks, as it is the case of urban networks. In this paper, a new approach to the current flow betweenness centrality for its practical application in urban networks with data is presented and discussed. The new centrality measure allows the estimation of pedestrian flow developed in urban networks, taking into account both the network topology and its associated data. In addition, its computational cost makes it suitable for application in networks with a large number of nodes. Some examples are studied in order to better understand the characteristics and behaviour of the proposed centrality in the context of the city.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:347:y:2019:i:c:p:600-615
    DOI: 10.1016/j.amc.2018.11.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300318310063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2018.11.032?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. 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.
    3. 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.
    4. Song Gao & Yaoli Wang & Yong Gao & Yu Liu, 2013. "Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality," Environment and Planning B, , vol. 40(1), pages 135-153, February.
    5. Sergio Porta & Vito Latora & Fahui Wang & Salvador Rueda & Emanuele Strano & Salvatore Scellato & Alessio Cardillo & Eugenio Belli & Francisco CÃ rdenas & Berta Cormenzana & Laura Latora, 2012. "Street Centrality and the Location of Economic Activities in Barcelona," Urban Studies, Urban Studies Journal Limited, vol. 49(7), pages 1471-1488, May.
    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. Winston Yap & Jiat-Hwee Chang & Filip Biljecki, 2023. "Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions," Environment and Planning B, , vol. 50(6), pages 1416-1437, July.
    2. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    3. Bowater, David & Stefanakis, Emmanuel, 2023. "Extending the Adapted PageRank Algorithm centrality model for urban street networks using non-local random walks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    4. Zhou, Bo & Song, Qiankun & Zhao, Zhenjiang & Liu, Tangzhi, 2020. "A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game," Applied Mathematics and Computation, Elsevier, vol. 371(C).
    5. Karolina Dudzic-Gyurkovich, 2023. "Study of Centrality Measures in the Network of Green Spaces in the City of Krakow," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    6. Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan & Teixeira, Rui, 2022. "Identifying critical and vulnerable links: A new approach using the Fisher information matrix," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    7. 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.
    8. Col, Alcebiades Dal & Petronetto, Fabiano, 2023. "Graph regularization centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(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. 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).
    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. 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.
    4. 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).
    5. 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).
    6. Giacopelli, G. & Migliore, M. & Tegolo, D., 2020. "Graph-theoretical derivation of brain structural connectivity," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    7. 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.
    8. 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.
    9. Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.
    10. da Cunha, Éverton Fernandes & da Fontoura Costa, Luciano, 2022. "On hypercomplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    11. 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.
    12. César Ducruet & Laurent Beauguitte, 2014. "Spatial Science and Network Science: Review and Outcomes of a Complex Relationship," Networks and Spatial Economics, Springer, vol. 14(3), pages 297-316, December.
    13. 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.
    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. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    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. Boeing, Geoff, 2019. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood," SocArXiv 7fxjz, Center for Open Science.
    18. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & Li, Rong & Wu, Jianjun, 2017. "Heuristic urban transportation network design method, a multilayer coevolution approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 71-83.
    19. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & He, Yuou & Li, Rong & Wu, Jianjun, 2018. "Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 800-817.
    20. 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.

    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:347:y:2019:i:c:p:600-615. 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.