Cascade Failure Model in Multimodal Transport Network Risk Propagation

He, Zhenggang; Guo, Jingni; Xu, Junxiang

doi:10.1155/2019/3615903

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…In fact, once a failure occurs in a multimodal transport network, its impact is spreading. This nature has been investigated by Wu et al, 55 Ruan et al, 56 and He et al 2 To calculate the importance rating of network failures more reasonably, we consider using multiple attributes to measure the impact of network failures, as shown in Figure 4. We consider using the DIFMA evaluation method to calculate the importance rating of network failures.…”

Section: Step 5: Importance Rating Of Network Failuresmentioning

confidence: 99%

“…, where θ X ( ) 2 represents the set of S, which contains two parameters. Assuming that S A B ( , ) satisfies the following conditions.…”

Section: ③ Similaritymentioning

confidence: 99%

“…Correspondingly, the network formed by the interconnection of two or more modes of transport is a multimodal transport network. Under the environment where the world is actively advocating green development and advancing sustainable construction, 1 multimodal transport is a low‐cost, low‐pollution, high‐efficiency, convenient, fast, and high‐quality transport method, 2 which has been vigorously promoted in many countries. China pointed out in the 2017 Annual Report on Government Information Disclosure by the Ministry of Transport that it is necessary to promote the development of multimodal transport and improve the overall transportation efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Risk assessment on multimodal transport network based on quality function deployment

Guo

et al. 2020

Int J Intell Syst

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As a necessary work before risk prevention and control, risk assessment has become increasingly important in multimodal transport networks. On the basis of the idea of quality function deployment (QFD), this paper combines fuzzy G1 method, two-tuple linguistic representation model, and dynamic intuitionistic fuzzy multi-attribute evaluation method to propose a new method for risk assessment. QFD is innovatively used to create an overall framework, which includes the basic importance rating of risk factors, the correlation between risk factors and network failures, the importance rating of network failures, and the comprehensive importance rating of risk factors. The comprehensive importance rating is regarded as an indicator to verify, improve, and modify the basic importance rating, which can make up for the disadvantage of the existing risk assessment methods in proving the credibility of the results. Its result is the key to judging the degree of influence of risk factors on network operation. This paper takes the multimodal transport network in the Sichuan-Tibet region of China as an example for analysis. The results are analyzed and discussed to prove the validity and advancement of the proposed method.

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Section: Step 5: Importance Rating Of Network Failuresmentioning

confidence: 99%

“…, where θ X ( ) 2 represents the set of S, which contains two parameters. Assuming that S A B ( , ) satisfies the following conditions.…”

Section: ③ Similaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk assessment on multimodal transport network based on quality function deployment

Guo

et al. 2020

Int J Intell Syst

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“…ey include transportation nodes, transportation connection edges, transit nodes, and transit connection edges [13], which are mainly composed of "service network" (local service network) and "internal operation network" (linehaul operations network) [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Multimodal Transport in China Based on Hesitation Fuzzy Multiattribute Decision-Making

Han

Wan

Zhou

et al. 2020

Mathematical Problems in Engineering

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Intermodal transportation, as an efficient form of transport organization, is a key technology and means for the logistics industry to balance transport costs and efficiency. How to deeply analyze the key factors constraining the level of development of multimodal transportation in China, building multimodal transportation development evaluation index system and evaluation model, evaluating comprehensively the multimodal transportation development level in China, and putting forward targeted countermeasures is of great practical significance. Based on the hesitant fuzzy multiattribute decision-making method, this paper analyzes the key factors influencing the level of multimodal transportation development from the perspective of sea-rail intermodal transport and further uses the interval number discretization and likelihood deviation maximization multiattribute decision-making method to construct a mixed-data index evaluation model, which is combined with data from 11 provinces (cities) in China. The study shows that there is a big gap among different provinces (cities) in China concerning the level of multimodal transportation development. Besides, the volume of intermodal container transportation and railway mileage are the core factors affecting the level of multimodal transportation development, and the results of the evaluation model can objectively reflect the level of multimodal transportation development and problems in each province (city). The research results further enrich the theoretical system of intermodal transportation development and provide a reference for the relevant management departments in formulating intermodal transportation plans and policies by putting forward pertinent suggestions in the field of management. In the future, the research direction should include the ecological environment, economic benefits, and other indicators into the evaluation system, expand the research scope of the modes of combined transportation such as public rail transport and public air transport, and improve the applicable scope of the evaluation model and the evaluation capability of specific scenarios.

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“…In the network, cascading failure may cause catastrophic consequences [7]. Thus, the recent study on cascading failure problems in practical network has aroused wide concern [19,33,22,24,9,6].…”

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confidence: 99%

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

Guo¹,

Xu²,

He³

et al. 2022

JIMO

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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), highdegree 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.

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Cascade Failure Model in Multimodal Transport Network Risk Propagation

Cited by 11 publications

References 10 publications

Risk assessment on multimodal transport network based on quality function deployment

Risk assessment on multimodal transport network based on quality function deployment

Evaluation of Multimodal Transport in China Based on Hesitation Fuzzy Multiattribute Decision-Making

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

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