Large subway systems as complex networks

Angeloudis, Panagiotis; Fisk, David

doi:10.1016/j.physa.2005.11.007

Cited by 214 publications

(114 citation statements)

References 7 publications

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“…According to Angeloudis and Fisk's study on the 20 largest subways throughout the world [24], urban rail transit networks can be depicted as complex networks, which possess the characteristics of high connectivity, but low maximum vertex degree and have typical features of both small-world and scale-free categories.…”

Section: Construction Of Urban Rail Transit Networkmentioning

confidence: 99%

Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China

2015

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Rail transit is developing rapidly in major cities of China and has become a key component of urban transport. Nevertheless, the security and reliability in operation are significant issues that cannot be neglected. In this paper, the network and station vulnerabilities of the urban rail transit system were analyzed based on complex network and graph theories. A vulnerability evaluation model was proposed by accounting metro interchange and passenger flow and further validated by a case study of Shanghai Metro with full-scale network and real-world traffic data. It is identified that the urban rail transit network is rather robust to random attacks, but is vulnerable to the largest degree node-based attacks and the highest betweenness node-based attacks. Metro stations with a large node degree are more important in maintaining the network size, while stations with a high node betweenness are critical to network efficiency and origin-destination (OD) connectivity. The most crucial stations in maintaining network serviceability do not necessarily have the highest passenger throughput or the largest structural connectivity. A comprehensive evaluation model as proposed is therefore essential to assess station vulnerability, so that attention can be placed on appropriate nodes within the metro system. The findings of this research are of both theoretical and practical significance for urban rail transit network design and performance evaluation.

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Section: Construction Of Urban Rail Transit Networkmentioning

confidence: 99%

Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China

2015

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“…The systematic analysis of PTN using tools of complex network theory dates back to the early 2000-s [23] and continues to this day [18,19,20,21,22,24]. It has been revealed that these networks share common statistical properties: they appear to be strongly correlated small-world structures with high values of clustering coefficients and comparatively low mean shortest path values.…”

Section: Database and Attack Scenarios Descriptionmentioning

confidence: 99%

Transportation Network Stability: A Case Study of City Transit

Berche

Ferber

Holovatch

et al. 2012

Advs. Complex Syst.

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The goals of this paper are to present criteria, that allow to a priori quantify the attack stability of real world correlated networks of finite size and to check how these criteria correspond to analytic results available for infinite uncorrelated networks. As a case study, we consider public transportation networks (PTN) of several major cities of the world. To analyze their resilience against attacks either the network nodes or edges are removed in specific sequences (attack scenarios). During each scenario the size S(c) of the largest remaining network component is observed as function of the removed share c of nodes or edges. To quantify the PTN stability with respect to different attack scenarios we use the area below the curve described by S(c) for c ∈ [0, 1] recently introduced (Schneider, C. M, et al., PNAS 108 (2011) 3838) as a numerical measure of network robustness. This measure captures the network reaction over the whole attack sequence. We present results of the analysis of PTN stability against node and link-targeted attacks.

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“…Others compare a set of few static network metrics, but without considering interactive/iterative/dynamic metrics sufficiently for much stronger attacks [26,[78][79][80][81][82] (see Section 4.2 for further discussion). For few studies, the authors do not reveal which kind of targeted attack they use: "We have simulated an attack on every network in our database by blocking travel through targeted stations" [83]. There are only a few notable exceptions, which correctly use interactive betweenness as a reference for network disruption simulation, e.g., [84][85][86][87].…”

Section: Common Pitfalls and Misleading Interpretationsmentioning

confidence: 99%

“…Existing studies often conclude with statements that the network is rather resilient to random failures, but more vulnerable to targeted attacks. These claims can be found on all kinds of transportation networks, including air transportation [94][95][96], railway-based systems [26,79,83,85,97], and others [98][99][100]. We only highlight two representative statements here; others follow very similar structures: "This scale-free structure has proved to be robust to random failure but vulnerable to targeted attack" [94].…”

Section: Common Pitfalls and Misleading Interpretationsmentioning

confidence: 99%

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

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The introduction of complex network concepts in the study of transportation systems has supposed a paradigm shift and has allowed understanding different transport phenomena as the emergent result of the interactions between the elements composing them. In spite of several notable achievements, lurking pitfalls are undermining our understanding of the topological characteristics of transportation systems. In this study, we analyse four of the most common ones, specifically related to the assessment of the scale-freeness of networks, the interpretation and comparison of topological metrics, the definition of a node ranking, and the analysis of the resilience against random failures and targeted attacks. For each topic we present the problem from both a theoretical and operational perspective, for then reviewing how it has been tackled in the literature and finally proposing a set of solutions. We further use six real-world transportation networks as case studies and discuss the implications of these four pitfalls in their analysis. We present some future lines of work that are stemming from these pitfalls and that will allow a deeper understanding of transportation systems from a complex network perspective.

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Large subway systems as complex networks

Cited by 214 publications

References 7 publications

Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China

Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China

Transportation Network Stability: A Case Study of City Transit

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

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