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doi: 10.3934/jimo.2020159

Research on cascading failure modes and attack strategies of multimodal transport network

1. 

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

2. 

National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China

3. 

School of Logistics, Chengdu University of Information Technology, Chengdu 610225, China

* Corresponding author: Wei Liao

Received  February 2020 Revised  September 2020 Published  March 2021

Cascading failure overall exists in practical network, which poses a risk of causing significant losses. Studying the effect of different cascading failure modes and attack strategies of the network is conducive to more effectively controlling the network. In the present study, the uniqueness of multimodal transport network is investigated by complying with the percolation theory, and a cascading failure model is built for the multimodal transport network by considering recovery mechanisms and dynamics. Under the three failure modes, i.e., node failure, edge failure and node-edge failure, nine attack strategies are formulated, consisting of random node attacking strategy (RNAS), high-degree attacking strategy (HDAS), high-closeness attacking strategy (HCAS), random edge attacking strategy (REAS), high-importance attacking strategy (HIAS1), high-importance attacking strategy (HIAS2), random node-edge attacking strategy (RN-EAS), high degree-importance1 attacking strategy (HD-I1AS), as well as high closeness-importance2 attacking strategy (HC-I2AS). The effect of network cascading failure is measured at the scale of the affected network that varies with the failure ratio and the network connectivity varying with the step. By conducting a simulation analysis, the results of the two indicators are compared; it is suggested that under the three failure modes, the attack strategies exhibiting high node closeness as the indicator always poses more effective damage to the network. Next, a sensitivity analysis is conducted, and it is concluded that HCAS is the most effective attack strategy. Accordingly, the subsequent study on the cascading failure of multimodal transport network should start with the nodes exhibiting high closeness to optimize the network.

Citation: Jingni Guo, Junxiang Xu, Zhenggang He, Wei Liao. Research on cascading failure modes and attack strategies of multimodal transport network. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020159
References:
[1]

Z. J. BaoY. J. Cao and L. J. Ding, Comparison of cascading failures in small-world and scale-free networks subject to vertex and edge attacks, Physica A: Statistical Mechanics and its Applications, 388 (2009), 4491-4498.   Google Scholar

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S. Dong, H. Wang, A. Mostafizi, et al., A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks, Physica A: Statistical Mechanics and its Applications, 538 (2020), 122971. Google Scholar

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Z. He, J. Guo and J Xu, Cascade failure model in multimodal transport network risk propagation, Mathematical Problems in Engineering, (2019), 1–7. Google Scholar

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P. Holme, B. J. Kim and C. N. Yoon, Attack vulnerability of complex networks, Physical Review E Statistical Nonlinear & Soft Matter Physics, 65 (2002), 56109. Google Scholar

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Z. JiangJ. Ma and Y. Shen, Effects of link-orientation methods on robustness against cascading failures in complex networks, Physica A: Statistical Mechanics and its Applications, 457 (2016), 1-7.   Google Scholar

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Z. Kong and E. M. Yeh, Correlated and cascading node failures in random geometric networks: A percolation view: International Conference on Ubiquitous & Future Networks, IEEE, (2012). Google Scholar

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M. Li, R. Liu and C. Jia, Cascading failures on networks with asymmetric dependence, EPL (Europhysics Letters), 108 (2014), 56002. Google Scholar

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R. R. Liu, C. X. Jia and Y. C. Lai, Asymmetry in interdependence makes a multilayer system more robust against cascading failure, Phys Rev E, 100 (2019), 52306. Google Scholar

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R. Parshani, S. V. Buldyrev and S. Havlin, Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition, Phys Rev Lett, 105 (2010), 48701. Google Scholar

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Z. Ruan, C. Song and X. Yang, Empirical analysis of urban road traffic network: A case study in Hangzhou city, China, Physica A: Statistical Mechanics and its Applications, 527 (2019), 121287. Google Scholar

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Y. Shen, G. Song and H. Xu, Model of node traffic recovery behavior and cascading congestion analysis in networks, Physica A: Statistical Mechanics and its Applications, (2019), 123422. Google Scholar

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J. SongE. Cotilla-Sanchez and G. Ghanavati, Dynamic Modeling of Cascading Failure in Power Systems, IEEE Transactions on Power Systems), 31 (2014), 2085-2095.   Google Scholar

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M. Tian, X. Wang and Z. Dong, Cascading failures of interdependent modular scale-free networks with different coupling preferences, EPL (Europhysics Letters), 111 (2015), 18007. Google Scholar

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M. Turalska, K. Burghardt and M. Rohden, Cascading failures in scale-free interdependent networks, Phys Rev E, 99 (2019), 32308. Google Scholar

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J. Wang and L. Rong, Cascade-based attack vulnerability on the US power grid, Safety Science, 47 (2009), 1332-1336.   Google Scholar

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E. WangC. Hong and X. Zhang, Cascading failures with coupled map lattices on Watts-Strogatz networks, Physica A: Statistical Mechanics and its Applications, 525 (2019), 1038-1045.   Google Scholar

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J. Wang and L. Rong, Effect attack on scale-free networks due to cascading failures, Chinese Physics LETTERS, 25 (2008), 3826. Google Scholar

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S. WangW. Lv and L. Zhao, Structural and functional robustness of networked critical infrastructure systems under different failure scenarios, Physica A: Statistical Mechanics and its Applications, 523 (2019), 476-487.   Google Scholar

[29]

F. WangT. Lixin and R. Du, The robustness of interdependent weighted networks, Physica A Statistical Mechanics & Its Applications, 508 (2018), 675-680.   Google Scholar

[30]

D. Witthaut and M. Timme, Nonlocal effects and countermeasures in cascading failures, Phys Rev E Stat Nonlin Soft Matter Phys, 92 (2015), 32809. Google Scholar

[31]

J. J. WuH. J. Sun and Z. Y. Gao, Cascading failures on weighted urban traffic equilibrium networks, Physica A: Statistical Mechanics and its Applications, 386 (2007), 407-413.   Google Scholar

[32]

J. WuJ. Zeng and Z. Chen, Effects of traffic generation patterns on the robustness of complex networks, Physica A: Statistical Mechanics and its Applications, 492 (2018), 871-877.   Google Scholar

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X. Wu, R. Gu and Y. Ji, Dynamic behavior analysis of an internet flow interaction model under cascading failures, Phys Rev E, 100 (2019), 22309. Google Scholar

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X. YuanY. Hu and H. E. Stanley, Eradicating catastrophic collapse in interdependent networks via reinforced nodes, Proceedings of the National Academy of Sciences, 114 (2017), 3311-3315.   Google Scholar

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D. Zhao, L. Wang and S. Li, Immunization of epidemics in multiplex networks, PLoS One, 9 (2014), e112018. Google Scholar

show all references

References:
[1]

Z. J. BaoY. J. Cao and L. J. Ding, Comparison of cascading failures in small-world and scale-free networks subject to vertex and edge attacks, Physica A: Statistical Mechanics and its Applications, 388 (2009), 4491-4498.   Google Scholar

[2]

S. R. Broadbent and J. M. Hammersley, Percolation processes, Mathematical Proceedings of the Cambridge Philosophical Society, 53 (1957), 629-641.  doi: 10.1017/S0305004100032680.  Google Scholar

[3]

S. V. BuldyrevR. Parshani and G. Paul, Catastrophic cascade of failures in interdependent networks, Nature, 464 (2009), 1025-1028.   Google Scholar

[4]

X. CaoC. Hong and W. Du, Improving the network robustness against cascading failures by adding links, Chaos, Solitions & Fractals, 57 (2013), 35-40.   Google Scholar

[5]

S. Chen, S. Pang and X. Zou, An LCOR model for suppressing cascading failure in weighted complex networks, Chinese Physics B, 22 (2013), 58901. Google Scholar

[6]

R. CohenK. Erez and D. Ben-Avraham, Breakdown of the Internet under Intentional Attack, Physical Review Letters, 86 (2001), 3682-3685.   Google Scholar

[7]

S. Dong, H. Wang, A. Mostafizi, et al., A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks, Physica A: Statistical Mechanics and its Applications, 538 (2020), 122971. Google Scholar

[8]

L. C. Freeman, Centrality in social networks' conceptual clarification, Social Networks, 1 (1979), 215-239.   Google Scholar

[9]

Z. He, J. Guo and J Xu, Cascade failure model in multimodal transport network risk propagation, Mathematical Problems in Engineering, (2019), 1–7. Google Scholar

[10]

P. Holme, B. J. Kim and C. N. Yoon, Attack vulnerability of complex networks, Physical Review E Statistical Nonlinear & Soft Matter Physics, 65 (2002), 56109. Google Scholar

[11]

Z. JiangJ. Ma and Y. Shen, Effects of link-orientation methods on robustness against cascading failures in complex networks, Physica A: Statistical Mechanics and its Applications, 457 (2016), 1-7.   Google Scholar

[12]

L. Jin, X. Wang and Y. Zhang, Cascading failure in multilayer networks with dynamic dependency groups, Chinese Physics B), (2018). Google Scholar

[13]

Z. Kong and E. M. Yeh, Correlated and cascading node failures in random geometric networks: A percolation view: International Conference on Ubiquitous & Future Networks, IEEE, (2012). Google Scholar

[14]

M. Li, R. Liu and C. Jia, Cascading failures on networks with asymmetric dependence, EPL (Europhysics Letters), 108 (2014), 56002. Google Scholar

[15]

R. R. Liu, C. X. Jia and Y. C. Lai, Asymmetry in interdependence makes a multilayer system more robust against cascading failure, Phys Rev E, 100 (2019), 52306. Google Scholar

[16]

A. E. Motter and Y. C. Lai, Cascade-based attacks on complex networks, Phys Rev E Stat Nonlin Soft Matter Phys, 66 (2002), 65102. Google Scholar

[17]

A. E. Motter and Y. Lai, Cascade-based attacks on complex network, Physical Review E Statistical Nonlinear & Soft Matter Physics, 66 (2002), 65102. Google Scholar

[18]

R. Parshani, S. V. Buldyrev and S. Havlin, Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition, Phys Rev Lett, 105 (2010), 48701. Google Scholar

[19]

Z. Ruan, C. Song and X. Yang, Empirical analysis of urban road traffic network: A case study in Hangzhou city, China, Physica A: Statistical Mechanics and its Applications, 527 (2019), 121287. Google Scholar

[20]

Y. Shen, G. Song and H. Xu, Model of node traffic recovery behavior and cascading congestion analysis in networks, Physica A: Statistical Mechanics and its Applications, (2019), 123422. Google Scholar

[21]

J. SongE. Cotilla-Sanchez and G. Ghanavati, Dynamic Modeling of Cascading Failure in Power Systems, IEEE Transactions on Power Systems), 31 (2014), 2085-2095.   Google Scholar

[22]

M. Stippinger and J. Kertész, Universality and scaling laws in the cascading failure model with healing, Physical Review E, 98 (2018). Google Scholar

[23]

M. Tian, X. Wang and Z. Dong, Cascading failures of interdependent modular scale-free networks with different coupling preferences, EPL (Europhysics Letters), 111 (2015), 18007. Google Scholar

[24]

M. Turalska, K. Burghardt and M. Rohden, Cascading failures in scale-free interdependent networks, Phys Rev E, 99 (2019), 32308. Google Scholar

[25]

J. Wang and L. Rong, Cascade-based attack vulnerability on the US power grid, Safety Science, 47 (2009), 1332-1336.   Google Scholar

[26]

E. WangC. Hong and X. Zhang, Cascading failures with coupled map lattices on Watts-Strogatz networks, Physica A: Statistical Mechanics and its Applications, 525 (2019), 1038-1045.   Google Scholar

[27]

J. Wang and L. Rong, Effect attack on scale-free networks due to cascading failures, Chinese Physics LETTERS, 25 (2008), 3826. Google Scholar

[28]

S. WangW. Lv and L. Zhao, Structural and functional robustness of networked critical infrastructure systems under different failure scenarios, Physica A: Statistical Mechanics and its Applications, 523 (2019), 476-487.   Google Scholar

[29]

F. WangT. Lixin and R. Du, The robustness of interdependent weighted networks, Physica A Statistical Mechanics & Its Applications, 508 (2018), 675-680.   Google Scholar

[30]

D. Witthaut and M. Timme, Nonlocal effects and countermeasures in cascading failures, Phys Rev E Stat Nonlin Soft Matter Phys, 92 (2015), 32809. Google Scholar

[31]

J. J. WuH. J. Sun and Z. Y. Gao, Cascading failures on weighted urban traffic equilibrium networks, Physica A: Statistical Mechanics and its Applications, 386 (2007), 407-413.   Google Scholar

[32]

J. WuJ. Zeng and Z. Chen, Effects of traffic generation patterns on the robustness of complex networks, Physica A: Statistical Mechanics and its Applications, 492 (2018), 871-877.   Google Scholar

[33]

X. Wu, R. Gu and Y. Ji, Dynamic behavior analysis of an internet flow interaction model under cascading failures, Phys Rev E, 100 (2019), 22309. Google Scholar

[34]

X. YuanY. Hu and H. E. Stanley, Eradicating catastrophic collapse in interdependent networks via reinforced nodes, Proceedings of the National Academy of Sciences, 114 (2017), 3311-3315.   Google Scholar

[35]

D. Zhao, L. Wang and S. Li, Immunization of epidemics in multiplex networks, PLoS One, 9 (2014), e112018. Google Scholar

Figure 1.  Multimodal transport network topology
Figure 2.  State change of node failure network
Figure 3.  State change of edge failure network
Figure 4.  State change of node-edge failure network
Figure 6.  Sensitivity analysis
Figure 5.  Impact of different attack strategies on the network
Table 1.  Network attacking strategy
Failure mode Attack strategy
Node failure RNAS HDAS HCAS
Edge failure REAS HIAS1 HIAS2
Node-edge failure RN-EAS HD-I1AS HC-I2AS
Failure mode Attack strategy
Node failure RNAS HDAS HCAS
Edge failure REAS HIAS1 HIAS2
Node-edge failure RN-EAS HD-I1AS HC-I2AS
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