Evaluating and optimizing resilience of airport pavement networks

Faturechi, Reza; Levenberg, Eyal; Miller-Hooks, Elise

doi:10.1016/j.cor.2013.10.009

Cited by 82 publications

(39 citation statements)

References 12 publications

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“…In simulation driven methods (Albores & Shaw, 2008;Spiegler, Naim, & Wikner, 2012), infrastructure models are subjected to hypothetical hazards and key performance indicators (e.g., percentage of on-time deliveries for supply chains) are tracked. Optimization modeling (Alderson, Brown, & Carlyle, 2015a;Faturechi, Levenberg, & Miller-Hooks, 2014), in contrast, aims to estimate least cost recovery or best-case performance for a system after damage.…”

Section: Evaluation Of Resiliencementioning

confidence: 99%

Review and Evaluation of the J100‐10 Risk and Resilience Management Standard for Water and Wastewater Systems

et al. 2019

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Risk analysis standards are often employed to protect critical infrastructures, which are vital to a nation's security, economy, and safety of its citizens. We present an analysis framework for evaluating such standards and apply it to the J100‐10 risk analysis standard for water and wastewater systems. In doing so, we identify gaps between practices recommended in the standard and the state of the art. While individual processes found within infrastructure risk analysis standards have been evaluated in the past, we present a foundational review and focus specifically on water systems. By highlighting both the conceptual shortcomings and practical limitations, we aim to prioritize the shortcomings needed to be addressed. Key findings from this study include (1) risk definitions fail to address notions of uncertainty, (2) the sole use of “worst reasonable case” assumptions can lead to mischaracterizations of risk, (3) analysis of risk and resilience at the threat‐asset resolution ignores dependencies within the system, and (4) stakeholder values need to be assessed when balancing the tradeoffs between risk reduction and resilience enhancement.

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Section: Evaluation Of Resiliencementioning

confidence: 99%

Review and Evaluation of the J100‐10 Risk and Resilience Management Standard for Water and Wastewater Systems

et al. 2019

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“…While several researches considered travel time parameter to estimate transportation system resilience through measuring travel time changes due to the disaster [8,24], others expressed resilience as the trip handling capacity of the network in post-disaster scenario compared to pre-disaster scenario [9,23,30,31]. As road link capacity improvement, travel time reduction, efficient and safe driving are expected to be key improvements CAV brings to the transportation system, in this study, the transportation system resilience was estimated based on the traffic flow variation, travel time variation, and capacity variation in a mixed-traffic environment.…”

Section: Chapter 3: Methodsmentioning

confidence: 99%

“…This factor expressed the criticality of nodes (i.e., intersections) in terms of disaster impact and can be used to prioritize most critical nodes for repair within the given budget. Several past research articles divided disaster durations in three stages [8,31]. However, in these three-stage classification, there is no defined stage which investigates the system's network condition during disaster landfall and after the recovery activities.…”

Section: Chapter 3: Methodsmentioning

confidence: 99%

“…Other than road-based network system, several studies evaluated the resilience of air transportation system. For example, [30,31] proposed an airline flow-based resilience model considering the number of arrival and departure flows on an air-hub (airport) to quantify the air transportation network system resilience. While Janić measured resilience for an air transport network (consisting of multiple airports) [30], Faturechi et al presented resilience model to measure resilience for the pavement network of an airport [31].…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

“…Rather, this phase represents the importance of understanding the pre-disaster normal traffic condition of the transportation network system and its performance level in terms of resilience parameter so that if any disaster happens the impact of disaster on system performance can be estimated and quantified. Though the preparedness activity such as: development of evacuation plan and training teams for rapid recovery [31], development of effective disaster management technique through pre-planned resource allocation [38], high resolution forecasting of the disaster to estimate the disaster impact [25,26] are critical components in the preparation phase before any disaster happening, this study has not investigated the implication of evacuation strategy. Rather, only the estimation of the resilience parameters is considered in preparation phase in mixed traffic environment.…”

Section: Preparation Phase (Phase 1 P1)mentioning

confidence: 99%

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Resilience Modeling of Surface Transportation System in Mixed Traffic Environment

Ahmed¹

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Resilience Modeling of Surface Transportation System in Mixed Traffic Environment Shofiq Ahmed Large-scale natural disasters challenge the resilience of surface transportation system. The objective of this research was to develop a resilience model of surface transportation system in mixed-traffic environment considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs are expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75% and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in terms of trips at any phase of disaster was compared to the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation was also estimated. The analysis showed that the resilience phenomenon of the transportation system improved with CAVs in respect of travel time and capacity improvement. The rate of improvement in link travel time for varied CAV penetration was almost identical for different disaster scenarios. For each disaster scenario, the individual link travel time reduced significantly with increased CAV penetration. However, higher penetration of CAVs (i.e., 50% or more), increased the recovery budget requirement. For example, the recovery budget needed for medium and large-scale disasters were 50% and 90% higher respectively compared to the recovery budget needed for a small-scale disaster. These higher costs were primarily needed for repair and replacement of intelligent infrastructure required for CAV.

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