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Are the queueing systems in practice random or uncertain? Evidence from online car-hailing data in Beijing

Author

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  • Yang Liu

    (Beihang University)

  • Zhongfeng Qin

    (Beihang University
    Key Laboratory of Complex System Analysis, Management and Decision (Beihang University))

  • Xiang Li

    (Chang’an University)

Abstract

In order to rationally characterize the nondeterministic phenomena in queueing systems, there exist two mathematical systems, one is probability theory concerned with the analysis of random phenomena and the other is uncertainty theory concerned with the analysis of uncertain phenomena. Before using the above two mathematical systems to model the real queueing systems, we often need to face such a question, are the real queueing systems random or uncertain? In order to answer this question, we collect the arriving times of passengers from online car-hailing platform in Beijing, and then analyze the collected data based on stochastic renewal process and uncertain renewal process. Finally, by comparing samples and confidence intervals of the total numbers of passengers arriving on the online car-hailing platform under two mathematical systems, we come to the conclusion that the queueing systems in the real world are uncertain rather than random.

Suggested Citation

  • Yang Liu & Zhongfeng Qin & Xiang Li, 2024. "Are the queueing systems in practice random or uncertain? Evidence from online car-hailing data in Beijing," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 497-511, December.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:4:d:10.1007_s10700-024-09430-0
    DOI: 10.1007/s10700-024-09430-0
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    References listed on IDEAS

    as
    1. Yang Liu & Baoding Liu, 2022. "Residual analysis and parameter estimation of uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 21(4), pages 513-530, December.
    2. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    3. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    4. Xiangfeng Yang & Hua Ke, 2023. "Uncertain interest rate model for Shanghai interbank offered rate and pricing of American swaption," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 447-462, September.
    5. Chen, Dan & Liu, Yang, 2023. "Uncertain Gordon-Schaefer model driven by Liu process," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    6. Farshid Mehrdoust & Idin Noorani & Wei Xu, 2023. "Uncertain energy model for electricity and gas futures with application in spark-spread option price," Fuzzy Optimization and Decision Making, Springer, vol. 22(1), pages 123-148, March.
    7. Zhe Liu, 2021. "Uncertain growth model for the cumulative number of COVID-19 infections in China," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 229-242, June.
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    Cited by:

    1. Hong Huang & Meihua Jiang & Yufu Ning & Shuai Wang, 2025. "A Structural Credit Risk Model with Jumps Based on Uncertainty Theory," Mathematics, MDPI, vol. 13(6), pages 1-19, March.

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