Research on risk propagation method of multimodal transport network under uncertainty

Jingni Guo, Junxiang Xu, Zhenggang He and Wei Liao

Physica A: Statistical Mechanics and its Applications, 2021, vol. 563, issue C

Abstract: Taking multimodal transport network as the research object, a quantitative method of risk propagation based on improved percolation theory is proposed. This paper analyzes the applicability and limitations of the percolation theory for this problem, and improves such four aspects of nodes and edges as the state, the initial load, the percolation probability and the evaluation index of percolation effect, so that they are universal to the risk propagation of the transport network. Taking Sichuan–Tibet​ region as an example for empirical analysis, this paper simulates and analyzes the risk propagation law of the multimodal transport network under different attack types and load preferences. The results show that the risk propagation effect in the multimodal transport network will become stronger with the increase of attack scale and attack intensity, and it will show an exponential growth trend with the increase of attack scale. When other conditions are the same, the order of strength of the three kinds of attack law according to the risk propagation effect is: the intentional attack based on the loads > the random attack > the intentional attack based on the failure probability; the risk propagation effect of the load with preferences is stronger than that without preferences. Therefore, managers can control the risk of multimodal transport network in Sichuan and Tibet from the perspective of controlling attack types and balancing load preference. The research improves the method of risk propagation simulation, makes up for the shortcomings of existing research which neglects the subjectivity of load and the dynamics of risk propagation, and makes it closer to the reality, which is of strong practical value and theoretical significance.

Keywords: Multimodal transport network; Risk propagation; Percolation theory; Uncertainty (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307925

DOI: 10.1016/j.physa.2020.125494

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