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Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks

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  • Homayoun Hamedmoghadam

    (Monash University)

  • Mahdi Jalili

    (RMIT University)

  • Hai L. Vu

    (Monash University)

  • Lewi Stone

    (RMIT University)

Abstract

Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.

Suggested Citation

  • Homayoun Hamedmoghadam & Mahdi Jalili & Hai L. Vu & Lewi Stone, 2021. "Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21483-y
    DOI: 10.1038/s41467-021-21483-y
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    Cited by:

    1. Wang, Xinhe & Wang, Zhen, 2022. "Bifurcation and propagation dynamics of a discrete pair SIS epidemic model on networks with correlation coefficient," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    2. Tang, Qianyong & Li, Huajiao & Qi, Yajie & Li, Yang & Liu, Haiping & Wang, Xingxing, 2023. "The reliability of the trade dependence network in the tungsten industry chain based on percolation," Resources Policy, Elsevier, vol. 82(C).
    3. Hui Chen & Sven Voigt & Xiaoming Fu, 2021. "Data-Driven Analysis on Inter-City Commuting Decisions in Germany," Sustainability, MDPI, vol. 13(11), pages 1-24, June.
    4. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. 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).
    6. Heng Ye & Zhiping Li & Guangyue Li & Yiran Liu, 2022. "Topology Analysis of Natural Gas Pipeline Networks Based on Complex Network Theory," Energies, MDPI, vol. 15(11), pages 1-20, May.

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