Resilience metrics and measurement methods for transportation infrastructure: the state of the art

Sun, Wenjuan; Bocchini, Paolo; Davison, Brian D.

doi:10.1080/23789689.2018.1448663

Cited by 167 publications

(51 citation statements)

References 215 publications

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“…A useful review from the transportation sector by Sun et al [46] pointed to metrics that are more relatable to criminogenic ecosystems by making a distinction between functionality and resilience metrics:…”

Section: Measuring Resiliencementioning

confidence: 99%

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

et al. 2020

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This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

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Section: Measuring Resiliencementioning

confidence: 99%

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

et al. 2020

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“…Literature reviews by Hosseini et al [10] and Sun et al [38] highlight the existence of several competing measures of infrastructure resilience. One of the most commonly used approaches is the loss triangle method, which quantifies resilience based on the loss of system performance integrated over the time it takes for the system to recover after a disruption [39].…”

Section: Resilience Measuresmentioning

confidence: 99%

Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models

Goldbeck

Angeloudis

Ochieng

2019

Reliability Engineering & System Safety

125

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Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London's metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities.

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“…The network performance is assessed through 11 quantitative indicators, which are classified into three types: network design, network centralization and operational indicators. A real-world European supply chain is considered as a case study [147] perform a review about resilience metrics from the transportation infrastructure point of view. The functionality of these networks is taken as a core to define two types of resilience metrics: functionality-related and socioeconomic metrics.…”

Section: Previous Literature Reviews and Positioning Of Our Workmentioning

confidence: 99%

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

Tordecilla

Juan

Montoya‐Torres

et al. 2021

Simulation Modelling Practice and Theory

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The design of supply chain networks (SCNs) aims at determining the number, location, and capacity of production facilities, as well as the allocation of markets (customers) and suppliers to one or more of these facilities. This paper reviews the existing literature on the use of simulation-optimization methods in the design of resilient SCNs. From this review, we classify some of the many works in the topic according to factors such as their methodology, the approach they use to deal with uncertainty and risk, etc. The paper also identifies several research opportunities, such as the inclusion of multiple criteria (e.g., monetary, environmental, and social dimensions) during the design-optimization process and the convenience of considering hybrid approaches combining metaheuristic algorithms, simulation, and machine learning methods to account for uncertainty and dynamic conditions, respectively.

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Resilience metrics and measurement methods for transportation infrastructure: the state of the art

Cited by 167 publications

References 215 publications

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

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