Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

Mou, Naixia; Sun, Shuyue; Yang, Tengfei; Wang, Zhipeng; Zheng, Yunhao; Chen, Jinhai; Zhang, Lingxian

doi:10.1109/access.2020.3028214

Cited by 39 publications

(21 citation statements)

References 57 publications

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“…If a city in a network tends to develop in groups with cities at a similar level and similar status, this indicates that the network has assortativity. Correspondingly, if the connections among cities surpass its status, cultural factors, and developmental differences, this indicates that the network has disassortativity [52]. The assortative network is easily affected by factors such as solidified connection paths and blocked structures, rendering them poor in terms of innovation and information penetration.…”

Section: Measurement Of Network Structural Characteristicsmentioning

confidence: 99%

“…On the contrary, due to its heterogeneity and openness, a disassortative network is resistant and resilient when facing risks in a region. The literature has used degree correlation, weighted degree-degree correlation, and other indicators to measure the assortativity of networks [52]; here, we used weighted degree correlation to characterize assortativity. If a node with a large neighbor-weighted average degree (NWAD) in a network tends to be connected to a node with a large weighted degree, this means that the network has assortativity; otherwise, it has disassortativity.…”

Section: Measurement Of Network Structural Characteristicsmentioning

confidence: 99%

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Resilience of Urban Network Structure in China: The Perspective of Disruption

Wei

Pan

2021

IJGI

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In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west; however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.

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Section: Measurement Of Network Structural Characteristicsmentioning

confidence: 99%

Section: Measurement Of Network Structural Characteristicsmentioning

confidence: 99%

Resilience of Urban Network Structure in China: The Perspective of Disruption

Wei

Pan

2021

IJGI

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“…For example, Orosz et al [ 16 ] explored the relationship between resilience and the minimal production flow rate of a process network. Mou et al [ 17 ] found that the resilience of a crude oil transportation network decreased at a steady rate during random attacks and decreased sharply during deliberate attacks. They also found that the density and centrality of the network were negatively correlated with resilience, while the connectivity and size of the network were positively correlated with resilience.…”

Section: Introductionmentioning

confidence: 99%

System resilience distribution identification and analysis based on performance processes after disruptions

Song

Li²

2022

PLoS ONE

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Resilience is a system’s ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system’s performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.

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“…It is generally known that the Corona Virus Disease 2019 (COVID- 19) was first identified and reported in Wuhan of China, the capital city of Hubei province with 11 million inhabitants and the most significant transport hub in Central China [6]. Due to its characteristics of person-to-person contact, extensive population flow has substantially increased social contacts in public, which caused COVID-19 to reach essentially everywhere [7].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have shown that the concept and definition of resilience can also be applied to regional networks [15]. In previous studies on the resilience of regional networks, some scholars focused on the resilience characteristics of finance networks [16], infrastructure networks [17], transport networks [18,19], and so forth. Although still gathering momentum, the previous studies mainly focus on theoretical exploration [20].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics and Resilience of Urban Network Structure during the Spring Festival Travel Rush: A Case Study of Urban Agglomeration in the Middle Reaches of Yangtze River in China

Wei

Pan

2021

Complexity

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With the increasing trend of globalization, large-scale and diffuse population flow have become vital carriers characterizing users' spatial behaviors. Network analysis provides a new perspective to uncover the topology and evolution of the population flow and understand its influence on regional development. By gathering the Autonavi migration index during the Spring Festival travel rush (SFTR) in 2019, 2020, and 2021, the population flow networks among 31 cities of urban agglomeration in the middle reaches of the Yangtze River were constructed to analyze spatiotemporal dynamic characteristics and explore the structure resilience. Results show that although the changing trends of population flow during the 40-day SFTR of 2019, 2020, and 2021 are consistent, the population floating scale in 2020 and 2021 shows remarkable abnormalities before and after the Spring Festival due to the need for prevention and control of COVID-19. The intensity of population floating of the regional urban network in 2020 was the weakest, and Changsha became the focus of most population flow, while Wuhan was the most advantageous city in 2019 and 2021. As the third core city in the regional network, the siphon effect of Nanchang was still weak. A situation of tripartite confrontation in the region is formed. However, the higher intensity of population flow in 2021 increased the instability of the regional urban network, potentially exposing the region to higher risks and pressures. Therefore, it is necessary to pay more attention to the peripheral cities to improve regional resilience.

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Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

Cited by 39 publications

References 57 publications

Resilience of Urban Network Structure in China: The Perspective of Disruption

Resilience of Urban Network Structure in China: The Perspective of Disruption

System resilience distribution identification and analysis based on performance processes after disruptions

Spatiotemporal Characteristics and Resilience of Urban Network Structure during the Spring Festival Travel Rush: A Case Study of Urban Agglomeration in the Middle Reaches of Yangtze River in China

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