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A study of fluctuations in subway traffic from the control properties of networks

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  • Yong, Nuo
  • Ni, Shunjiang
  • Shen, Shifei
  • Ji, Xuewei

Abstract

Menezes and Barabasi have once developed a framework by mean–variance analysis to investigate the relationship between the average flux and fluctuations and found a scaling-law behavior in two distinct phase. By then most related researches dedicated to understanding the scaling laws through the internal/external forces of the system. However, as for subway system, the demand of transporting a large amount of passengers during peak hours is a necessity. Thus, alternating the phase or reducing the impact of external forces by cutting down the total passengers is completely unrealistic. The more operable approaches are specifically regulating the distribution of passenger flow in order to drive the system into a more stable phase. In this paper, we carried out the mean–variance analysis on subway traffic and dedicated to discover the effect of spatial–temporal distributions of passenger flow on the fluctuations in subway traffic. By analyzing the control properties of the transit networks constructed according to different passenger flow threshold, it is found that the control mode of transit networks might differ the phase change of fluctuations, which proposed a new perspective of controlling the fluctuations in subway traffic.

Suggested Citation

  • Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Ji, Xuewei, 2020. "A study of fluctuations in subway traffic from the control properties of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    • Handle: RePEc:eee:phsmap:v:550:y:2020:i:c:s0378437120302338
    DOI: 10.1016/j.physa.2020.124517
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    References listed on IDEAS

    as
    1. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Ji, Xuewei, 2016. "An understanding of human dynamics in urban subway traffic from the Maximum Entropy Principle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 222-227.
    2. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    3. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Chen, Peng & Ji, Xuewei, 2018. "Uncovering stable and occasional human mobility patterns: A case study of the Beijing subway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 28-38.
    4. Tao Jia & Yang-Yu Liu & Endre Csóka & Márton Pósfai & Jean-Jacques Slotine & Albert-László Barabási, 2013. "Emergence of bimodality in controlling complex networks," Nature Communications, Nature, vol. 4(1), pages 1-6, October.
    5. Kosmas Kosmidis & Moritz Beber & Marc-Thorsten Hütt, 2015. "Network Heterogeneity And Node Capacity Lead To Heterogeneous Scaling Of Fluctuations In Random Walks On Graphs," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(01n02), pages 1-22.
    6. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    7. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
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    Cited by:

    1. Yu, Xiaoyao & Liang, Yongqing & Wang, Xiaomeng & Jia, Tao, 2021. "The network asymmetry caused by the degree correlation and its effect on the bimodality in control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).

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