Networked analysis of the Shanghai subway network, in China

Zhang, Jianhua; Xu, Xiaoming; Liu, Hong; Wang, Shuliang; Qi, Fei

doi:10.1016/j.physa.2011.06.022

Cited by 138 publications

(71 citation statements)

References 38 publications

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“…Previous literature [19,27] used the largest connected cluster (LCC) to evaluate the functional vulnerability of urban rail transit networks, which was defined as the size of the largest sub-network after being attacked. However, this parameter is not suitable to be applied to urban rail transit networks, as trains may still be in operation on each sub-network, even if the network is divided into multiple isolated sub-networks.…”

Section: Functional Vulnerabilitymentioning

confidence: 99%

“…Wang [18] constructed the topological model of the Beijing transit network and simulated the network efficiency under various attacks. Zhang et al [19] measured the topological characteristics and functional properties of the Shanghai metro system. Nevertheless, these studies simplified the urban rail transit networks with graph theory and, therefore, lacked consideration of the significant properties of rail transit systems, such as the ability to transfer, etc.…”

Section: Introductionmentioning

confidence: 99%

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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: Functional Vulnerabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…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]. "This study indicates that the subway network is robust against random attacks but fragile for malicious attacks" [79].…”

Section: Common Pitfalls and Misleading Interpretationsmentioning

confidence: 86%

“…Some of the studies [76,77] only evaluate DEG of nodes for designing a targeted attack, e.g., stating that "for the selective attack strategy, we remove some nodes with higher degrees according to their degree order from high to low" [77]. 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].…”

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%

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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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The Design of Rapid Transit Networks

Laporte

Mesa

2019

Location Science

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Networked analysis of the Shanghai subway network, in China

Cited by 138 publications

References 38 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

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

The Design of Rapid Transit Networks

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