Analysis of the influence of urban traffic network robustness under directional attack disaster–take Zhengzhou as an example
Author:
Funder
Scientific Research Project of Education Department of Liaoning Province
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-023-02179-6.pdf
Reference44 articles.
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2. Cai J, Deng W (2019) Complex characteristics and cascading failure robustness analysis of Changsha underground network. J Railway Sci Eng 16(06):1587–1596
3. Cats O, Krishnakumari P (2020) Metropolitan rail network robustness. Phys A Stat Mech Appl 549:124317
4. Dazhou L, Chuan L, Wei G, et al. (2020) Capsules TCN network for Urban computing and intelligence in urban traffic prediction. Wirel Commun Mobile Comput 2020
5. Ding K, Huang NJ (2006) Global robust exponential stability of interval general BAM neural network with delays. Neural Process Lett 23(2):171–182
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