Simulation Study on Cascading Failure of Multimodal Transport Network

Jingni Guo,Zhenggang He,Wei Liao,Junxiang Xu,Xingju Wang

doi:10.1155/2020/3976910

Abstract

Cascading failure in multimodal transport network may cause huge economic loss and social impact, which has gradually attracted public attention. In view of the coupling effect of nodes in multimodal transport network and the higher complexity of cascading failure process, the concepts of node correlation degree and node cooperation degree are proposed to characterize the characteristics of the network, and a logit model is introduced to calculate the initial load of nodes. In the case of ignoring network interruption, we propose two load redistribution methods: local allocation and global-local allocation. Taking the multimodal transport network in Sichuan–Tibet region of China as an example, the cascading failure effect of multimodal transport network in Sichuan–Tibet region is quantified by sensitivity analysis. The results show that when the load of the multimodal transport network in Sichuan–Tibet region exceeds the maximum capacity but does not exceed 150%∼170% of the network capacity, the network can still operate normally. In addition, the nodes in the multimodal transport network should have 0.3∼0.5 scalable space. In the cascading failure control method, load redistribution based on global-local allocation can minimize the impact of node overload.

Highlights

Cascading failure theory was first studied by Motter and Lai [1], and the classical ML model was proposed
Zhang et al [9] analyzed the effects of different parameters on cascading failure process; Qian [10] studied the effects of network time delay and self-healing on cascading failure process
E structure of this paper is as follows: Section 2 introduces the definitions, including node relevance and node collaboration; in Section 3, the cascading failure model is established; in Section 4, the empirical analysis of the multimodal transport network in Sichuan–Tibet region of China is performed; and Section 5 summarizes the work of this paper

Summary

Introduction

Cascading failure theory was first studied by Motter and Lai [1], and the classical ML model was proposed. Considering the influence of self-healing ability of nodes and delay time factor on the disaster spreading mechanism, taking the number of collapse nodes and the repair rate of nodes as evaluation factors, the influence of different parameter values on the network was studied; Liu et al [7] studied the cascading failure model in the traffic network under the emergency and determined the initial load by the incremental loading method under the multipath probability. It can be seen that the relevant researches focus on: first, the determination of initial load and update mechanism; second, the research of load redistribution after node failure; and third, the influence of different factors on cascading failure effect. E structure of this paper is as follows: Section 2 introduces the definitions, including node relevance and node collaboration; in Section 3, the cascading failure model is established; in Section 4, the empirical analysis of the multimodal transport network in Sichuan–Tibet region of China is performed; and Section 5 summarizes the work of this paper

Definitions

Cascading Failure Model

Case Study

Findings

Conclusion