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research-article

Resilience of Metrorail Networks: Quantification with Washington D.C. as a case study

[+] Author and Article Information
Yalda Saadat

Department of Civil and Environmental Engineering, Center for Technology and Systems Management, University of Maryland, College Park, MD 20742
yaldas@umd.edu

Bilal M. Ayyub

Professor, Department of Civil and Environmental Engineering, Center for Technology and Systems Management, University of Maryland, College Park, MD 20742
ba@umd.edu

Yanjie Zhang

Department of Geotechnical Engineering, Key Laboratory of Geotechnical and Underground Engineering, Tongji University, Shanghai 200092, China
7zhangyj@tongji.edu.cn

Dongming Zhang

Assistant Professor, Department of Geotechnical Engineering, Key Laboratory of Geotechnical and Underground Engineering, Tongji University, Shanghai 200092, China
09zhang@tongji.edu.cn

Hongwei Huang

Professor, Department of Geotechnical Engineering, Key Laboratory of Geotechnical and Underground Engineering, Tongji University, Shanghai 200092, China
huanghw@tongji.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4044038 History: Received August 08, 2018; Revised February 12, 2019

Abstract

According to the United Nation's Department of Economic and Social Affairs Population Division, 66 percent of the world's population will reside in urban areas by 2050; a boost from 30 percent in 1950. Urbanization has indeed triumphed and has increased demand for infrastructure systems including metrorail networks, one of the most tangible examples of complex transportation infrastructure systems. A synergistic rapid growth of urban population concurrent with great increases in metrorail use may lead to perturbations in such a system. Considering such perturbations and developing appropriate resilience measurements are paramount to the design of sustainable and resilient systems. As such, developing resilience metrics in a structured manner is necessary. Using Washington D.C. Metro as a case study, this paper examines metrorail network resilience with well-defined relationships to vulnerability and network efficiency. This examination includes developing a metro rail model in a graph form and obtaining its basic features by network topology analysis. The analytical work demonstrates that the Washington D.C. Metro has a high level of vulnerability in the case of disruptive events. Evaluation of the efficiency and vulnerability of a metrorail network after disruption requires consideration of two primary failure events, i.e., the failure of a metro station or the failure of a metro segment between stations. Vulnerability evaluation identifies the most critical stations and rail segments as illustrated using the Washington D.C. Metro network. Such an assessment offers a logical basis to enhance system resilience and develop post-failure recovery strategies.

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