Network efficiency and vulnerability analysis using the flow‐weighted efficiency measure

Nistor, Marian Sorin; Pickl, Stefan; Raap, Manon; Zsifkovits, Martin

doi:10.1111/itor.12384

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Based on [6,7], the nodal efficiency measure is applied by calculating the length of the shortest paths from one node to all the others in the network. Its formula E v V G i ( ) ( ) [6,7] for one node v i in the network G is defined by:…”

Section: Efficiency Measurementioning

confidence: 99%

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Analyzing vulnerabilities of the German highspeed train network using quantitative graph theory

Wang¹,

Zsifkovits²,

Pickl³

2018

Int. J. SAFE

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The German high-speed train system (ICE) as one of the critical infrastructures is mapped into a distance-weighted undirected network. The aim of the analysis is to make full use of quantitative graph theory in order to analyze the vulnerabilities of the network and to detect the centers and hubs of the system. When conducting network analysis of railways, there is a tradition of such an analysis that the betweenness centrality measure and the efficiency measure would be applied; however, based on these two measures, we offer a new promising one that we call betweenness-efficiency vulnerability measure, which can be used to detect the most vulnerable nodes on an aggregated level. By analyzing and comparing the results of these three measures, highly vulnerable stations are identified, which therefore have more potential to harm the overall system in case of disruption. This can help decision-makers to understand the structure, behavior and vulnerabilities of the network more directly from the point of view of quantitative graph theory. Finally, the problem of adapting a new vulnerability measure to this kind of system is discussed.

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Section: Efficiency Measurementioning

confidence: 99%

“…Before carrying out the vulnerability analysis of the ICE network, we introduce the vulnerability index network residual closeness [7]. This measure is based on how the closeness of a network would change after the removal of nodes or edges.…”

Section: Vulnerability Analysismentioning

confidence: 99%

Analyzing vulnerabilities of the German highspeed train network using quantitative graph theory

Wang¹,

Zsifkovits²,

Pickl³

2018

Int. J. SAFE

Self Cite

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show abstract

“…Reliability measures and indices [9], analyze how much disruption a network can handle before failure, while the resilience ones [12], analyze how much time the network needs to return to the operating state after disruption. One application for the flow measures and indices [14] is to analyze the flow efficiency of the network, and to quantify the drop in efficiency in case of disruption. With the continued advancement in transportation network analysis, decision makers need more support to interpret the results, to avoid overloading the process with too much information structured in large tables and overcrowded plots.…”

Section: Background and State-of-the-artmentioning

confidence: 99%

“…Many studies tried to analyze different aspects of these type of networks like network centrality [2][3][4][5], vulnerability [6][7][8], reliability [9,10], resilience [11][12][13], flows [14][15][16], etc., and there has been a considerable progress in the past few decades. However, most of the studies are at a theoretical level and the decision-makers need a consistent background in the field of graph theory to perform such analyses and to accurately interpret the results.…”

Section: Introductionmentioning

confidence: 99%

Proof of Concept for a Visual Analytics Dashboard for Transportation Network Analysis

Nistor¹

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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This paper discusses the latest developments in the field of visual analytics, and the role of network analysis for transportation systems. Multilayer and multiplex based visualizations are considered reliable solutions for handling the information overload the decision makers are facing in the addressed domain. The existing tools matching these requirements are briefly reviewed. Then, a proof of concept for a dashboard is presented focusing on a transportation network analysis with multiple network measures and indices in a multiplex visualization.

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“…This method can effectively decompose the topological graph and be used to analyze the vulnerability of the new and old metro networks in Delhi. Nistor et al (2019) proposed a flow-weighted efficiency metric to assess network vulnerability and applied it to the Munich Metro for analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on entropy weight multiple criteria decision‐making evaluation of metro network vulnerability

Chen

Mao

2022

Int Trans Operational Res

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When the subway network system has insufficient resilience and strong vulnerability, the network operation function will be greatly attenuated or even lost due to the failure of key nodes or lines. Evaluating the vulnerability of the metro network is one of the keys to prevent and control risks. This paper proposes an entropy weight multiple criteria decision‐making evaluation method that combines the vulnerability indexes of agglomeration, network efficiency, and network traffic with the multiple criteria decision‐making method, classifies the vulnerability of subway stations with a clustering idea, and analyzes the most vulnerable area of the Beijing Metro network with the Newman fast algorithm. The results show that this method can evaluate the node vulnerability of metro networks more accurately and effectively by considering the topological vulnerability, functional vulnerability, and human flow function vulnerability of the networks. According to the node vulnerability value, the stations can be divided into five grades: The station at the intersection of the loop and radial lines and its adjacent stations have a higher vulnerability level. The vulnerability level of the downtown core area and suburban line terminal stations is relatively low, and the areas with the greatest vulnerability of the Beijing Metro network are mainly located on the east and west sides of the core city.

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Network efficiency and vulnerability analysis using the flow‐weighted efficiency measure

Cited by 6 publications

References 20 publications

Analyzing vulnerabilities of the German highspeed train network using quantitative graph theory

Analyzing vulnerabilities of the German highspeed train network using quantitative graph theory

Proof of Concept for a Visual Analytics Dashboard for Transportation Network Analysis

Research on entropy weight multiple criteria decision‐making evaluation of metro network vulnerability

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