Volume 24 Issue 4
Aug.  2024
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DU Yong-jun, WANG Ning, ZHANG Pan, CAI Zhi-qiang, QIAO Xiong. Importance evaluation of edges in transportation network under dynamic and randomly disruptive event[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 184-194. doi: 10.19818/j.cnki.1671-1637.2024.04.014
Citation: DU Yong-jun, WANG Ning, ZHANG Pan, CAI Zhi-qiang, QIAO Xiong. Importance evaluation of edges in transportation network under dynamic and randomly disruptive event[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 184-194. doi: 10.19818/j.cnki.1671-1637.2024.04.014

Importance evaluation of edges in transportation network under dynamic and randomly disruptive event

doi: 10.19818/j.cnki.1671-1637.2024.04.014
Funds:

National Natural Science Foundation of China 72161025

National Natural Science Foundation of China 72371035

Key Research and Development Program of Shaanxi Province 2023-YBGY-143

Foundation of China Scholarship Council 202308620190

More Information
  • Author Bio:

    DU Yong-jun(1977-), male, associate professor, PhD, yjdu@lut.edu.cn

    WANG Ning(1982-), male, professor, PhD, ningwang@chd.edu.cn

  • Received Date: 2024-01-13
    Available Online: 2024-09-26
  • Publish Date: 2024-08-28
  • A dynamic Bayesian importance measure method was proposed for edge importance evaluation of transpartation network. The random process theory was applied to characterize the generation process of external disruptive events, and a transportation network reliability model was established. Probabilistic techniques were utilized to obtain a formula of dynamic Bayesian importance measure of each network edge, and a maximum value for the importance measure and corresponding maximum edges were determined. Based on the formula, an numerical algorithm was developed to evaluate the Bayesian importance measure of each edge at different times. An actual case of a transportation network was introduced, and the dynamic random disruptive shock process incurred by the edges was a saturated non-time homogeneous Poisson counting process with given scale parameters and shape parameters. The calculation method of dynamic Bayesian importance measure was demonstrated, and the sensitivity analysis of the scale parameter and shape parameter of the importance ranking of the connected edges was made. Research results show that, regardless of the changes in random disruptive events from the external environment, the one-edge cut in the network remains the most important edge, which verifies the correctness of the theoretical analysis. Considering external random disruptive events and the network structure, the Bayesian importance measure can timely and accurately identify the importance of all edges, which fills the gap left by the traditional static measure for edge importance that only considers the position of an edge. As the values of the scale parameters and shape parameters become larger, the importance ranking of the two edges changes faster.

     

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  • [1]
    GERTSBAKH I, SHPUNGIN Y. Network Reliability: A Lecture Course[M]. Berlin: Springer, 2020.
    [2]
    ZHOU Yao-ming, WANG Jun-wei, YANG Hai. Resilience of transportation systems: concepts and comprehensive review[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4262-4276. doi: 10.1109/TITS.2018.2883766
    [3]
    HOU Ben-wei, LI Xiao-jun, HAN Qiang, et al. Post-earthquake connectivity and travel time analysis of highway networks based on Monte Carlo simulation[J]. China Journal of Highway and Transport, 2017, 30(6): 287-296. (in Chinese) doi: 10.3969/j.issn.1001-7372.2017.06.012
    [4]
    ZHANG Ming-hang, WEI Jin, FAN Wei-li, et al. Travel time reliability of OD routes in urban rail transit network[J]. Urban Transport of China, 2023, 21(2): 109-117, 72. (in Chinese)
    [5]
    YANG Jing-feng, ZHU Da-peng, ZHAO Rui-lin. Evaluation of station importance and cascading failure resistance analysis of urban rail transit network[J]. China Safety Science Journal, 2022, 32(8): 161-167. (in Chinese)
    [6]
    MA Fei, ZHAO Cheng-yong, SUN Qi-peng, et al. Integrated resilience of urban rail transit network with active passenger flow restriction under major public health disasters[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 208-221. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2023.01.016
    [7]
    BAI Jian-ming, LI Ze-hui, KONG Xin-bing. Generalized shock models based on a cluster point process[J]. IEEE Transactions on Reliability, 2006, 55(3): 542-550. doi: 10.1109/TR.2006.879661
    [8]
    CHA J H, FINKELSTEIN M, LEVITIN G. Bivariate preventive maintenance for repairable systems subject to random shocks[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2017, 231(6): 643-653. doi: 10.1177/1748006X17721797
    [9]
    MALLOR F, OMEY E. Shocks, runs and random sums[J]. Journal of Applied Probability, 2001, 38(2): 438-448. doi: 10.1239/jap/996986754
    [10]
    LIU Zhen-yu, MA Xiao-bing, SHEN Li-juan, et al. Degradation- shock-based reliability models for fault-tolerant systems[J]. Quality and Reliability Engineering International, 2016, 32(3): 949-955. doi: 10.1002/qre.1805
    [11]
    GUT A. Mixed shock models[J]. Bernoulli, 2001, 7(3): 541-555. doi: 10.2307/3318501
    [12]
    WANG Xiao-yue, ZHAO Xian, SUN Jing-lei. A compound negative binomial distribution with mutative termination conditions based on a change point[J]. Journal of Computational and Applied Mathematics, 2019, 351: 237-249. doi: 10.1016/j.cam.2018.11.009
    [13]
    FINKELSTEIN M, GERTSBAKH I. 'Time-free' preventive maintenance of systems with structures described by signatures[J]. Applied Stochastic Models in Business and Industry, 2015, 31(6): 836-845. doi: 10.1002/asmb.2111
    [14]
    KUO W, ZHU X Y. Importance Measures in Reliability, Risk and Optimization[M]. Chichester: John Wiley and Sons, 2012.
    [15]
    WANG Xiao-fan, LI Xiang, CHEN Guan-rong. Network Science: An Introduction[M]. Beijing: Higher Education Press, 2012. (in Chinese)
    [16]
    DU Yong-jun, HUI Shu-peng, CAI Zhi-qiang, et al. Evaluating of importance measures for K-terminal network with the probability distribution of failed edges[J]. Operations Research and Management Science, 2022, 31(6): 111-116. (in Chinese)
    [17]
    YUAN Guang, KONG De-wen, SUN Li-shan, et al. Study on method for discriminating importance of urban transport hub from perspective of super network[J]. Journal of Highway and Transportation Research and Development, 2023, 40(1): 192-199. (in Chinese)
    [18]
    KOPSIDAS A, KEPAPTSOGLOU K. Identification of critical stations in a metro system: a substitute complex network analysis[J]. Physica A: Statistical Mechanics and its Applications, 2022, 596: 127123. doi: 10.1016/j.physa.2022.127123
    [19]
    CHEN Wei-wei, ZHANG Fu-gui, ZHAO Xiao-bo. Topological structure model and node importance analysis of rail transit network[J]. Journal of Chongqing Jiaotong University (Natural Science), 2019, 38(7): 107-113. (in Chinese) doi: 10.3969/j.issn.1674-0696.2019.07.18
    [20]
    MENG Yang-yang, TIAN Xiang-liang, LI Zhong-wen, et al. Comparison analysis on complex topological network models of urban rail transit: a case study of Shenzhen Metro in China[J]. Physica A: Statistical Mechanics and its Applications, 2020, 559: 125031. doi: 10.1016/j.physa.2020.125031
    [21]
    WU Xing-tang, DONG Hai-rong, TSE C K, et al. Analysis of metro network performance from a complex network perspective[J]. Physica A: Statistical Mechanics and its Applications, 2018, 492: 553-563. doi: 10.1016/j.physa.2017.08.074
    [22]
    MA Chao-qun, ZHANG Shuang, CHEN Quan, et al. Characteristics and vulnerability of rail transit network besed on perspective of passenger flow characteristics[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 208-216. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.05.017
    [23]
    ZHOU Min, WANG Hai-ming, YANG Zhen-long, et al. Evaluation ranking of Chengdu metro TOD stations based on entropy weight-TOPSIS[J]. Railway Transport and Economy, 2023, 45(4): 150-156. (in Chinese)
    [24]
    XUE Feng, HE Chuan-lei, HUANG Qian. Identification of key nodes in Chengdu metro network and analysis of network performance[J]. China Safety Science Journal, 2019, 29(1): 93-99. (in Chinese)
    [25]
    WANG Ting, ZHANG Yong, ZHOU Ming-ni, et al. Identification of key nodes of urban rail transit integrating network topology characteristics and passenger flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 201-211. (in Chinese)
    [26]
    LYU Biao, GAO Zi-qiang, LIU Yi-liu. Evaluation of road transportation system resilience and link importance[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 114-121. (in Chinese)
    [27]
    FRENKEL I B, KARAGRIGORIOU A, LISNIANSKI A, et al. Applied Reliability Engineering and Risk Analysis: Probabilistic Models and Statistical Inference[M]. Chichester: John Wiley and Sons, 2014.
    [28]
    PEETA S, SIBEL SALMAN F, GUNNEC D, et al. Pre- disaster investment decisions for strengthening a highway network[J]. Computers and Operations Research, 2010, 37(10): 1708-1719. doi: 10.1016/j.cor.2009.12.006
    [29]
    DU Yong-jun, ZHANG Pan, CAI Zhi-qiang. Analysis of link interaction regarding network failure subject to a saturated nonhomogeneous Poisson process[J]. Control and Decision, 2024, 39(1): 180-188. (in Chinese)
    [30]
    ROSS S M. Introduction to Probability Models[M]. Berlin: Elsevier, 2010.
    [31]
    CHANG H W, HWANG F K. Rare-event component importance for the consecutive-k system[J]. Naval Research Logistics, 2002, 49(2): 159-166. doi: 10.1002/nav.10001

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