Volume 40 Issue 1
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XIAO Qin, LUO Fan. Propagation Mechanism of Safety Risk During Take-off and Landing of Amphibious Seaplanes Based on D-SEIRS Model[J]. Journal of Transport Information and Safety, 2022, 40(1): 1-9. doi: 10.3963/j.jssn.1674-4861.2022.01.001
Citation: XIAO Qin, LUO Fan. Propagation Mechanism of Safety Risk During Take-off and Landing of Amphibious Seaplanes Based on D-SEIRS Model[J]. Journal of Transport Information and Safety, 2022, 40(1): 1-9. doi: 10.3963/j.jssn.1674-4861.2022.01.001
shu

Propagation Mechanism of Safety Risk During Take-off and Landing of Amphibious Seaplanes Based on D-SEIRS Model

doi: 10.3963/j.jssn.1674-4861.2022.01.001
  • 1.

    School of Management, Wuhan Institute of Technology, Wuhan 430205, China

  • 2.

    School of Management, Wuhan University of Technology, Wuhan 430070, China

  • Received Date: 2021-08-27
    Available Online: 2022-03-31
    Abstract
  • It is of great importance to study the safety risk of amphibious seaplanes during their take-off and landing, since accidents occur frequently in these two phases. Based on the SEIRS model for disease transmission, considering the propagation and delay mechanism on safety risk of amphibious seaplane during take-off and landing, a risk propagation delay(D-SEIRS)model based on a scale-free network is developed to study the propagation mechanism of safety risk during the take-off and landing of amphibious seaplanes. The Routh-Hurwitz Criterion is used to analyze the stability of the equilibrium in the proposed model and solve for the steady-state density(SSD)and basic regeneration number of the proposed model. A numeric simulation based on the MATLAB software is performed using the proposed model, which discloses the dynamic propagation law of the safety risk during the take-off and landing of amphibious seaplanes. Study results show that both the effective propagation rate(EPR) and the propagation delay time(PDT)can lead to the increase of the steady-state density of the infected nodes of the network; the propagation delay can reduce the risk propagation threshold in the network and accelerate the emerging of risk outbreak state; the propagation rates of both latent nodes and infected nodes will lead to an increase in the steady-state density of infected nodes and latent nodes, and the effective propagation rate of latent nodes has a more prominent impact on risk propagation over the network than that of the infected nodes.

     

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