Operational models of infrastructure resilience

Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

Can Blue-Green Infrastructure enhance resilience in urban drainage systems during failure conditions?

The need to enhance the resilience of urban drainage systems (UDSs) in view of emerging global climate change and urbanisation threats is well recognised. Blue-Green Infrastructure (BGI) provides a suitable strategy for building the resilience of existing UDSs. However, there are limited quantitative studies that provide evidence of their effectiveness for increased uptake in cities. In this research, coupled one-dimensional-two-dimensional (1D-2D) modelling is applied to assess the effectiveness of BGI that include rainwater harvesting systems, infiltration trenches, bioretention cells, and detention ponds using two case study UDSs located in Kampala that experience catastrophic pluvial flooding caused by extreme rainfall. The resulting flooding impacts are quantified considering 'failed' and 'non-failed' UDS initial states, using total flood volume and average flood duration as system performance indicators. The study results suggest that spatially distributed rainwater harvesting systems singularly lead to a reduction in total flood volume and average flood duration of 16-45% and 18-24% in the case study UDSs, respectively. Furthermore, the study results suggest that BGIs are more effective during moderate rainfall (T < 10 years). Based on the study findings, city scale implementation of multifunctional rainwater harvesting systems is recommended as a suitable strategy for enhancing UDSs' resilience.

What is wrong with the Mission Dependency Index for US federal infrastructure decisions?

The Mission Dependency Index (MDI) is a risk metric used by US military services and federal agencies for guiding operations, management, and funding decisions for facilities. Despite its broad adoption for guiding the expenditure of billions in federal funds, several studies on MDI suggest it may have flaws that limit its efficacy. We present a detailed technical analysis of MDI to show how its flaws impact infrastructure decisions. We present the MDI used by the US Navy and develop a critique of current methods. We identify six problems with MDI that stem from its interpretation, use, and mathematical formulation, and we provide examples demonstrating how these flaws can bias decisions. We provide recommendations to overcome flaws for infrastructure risk decision making but ultimately recommend the US government develop a new metric less susceptible to bias.

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

Thermal Degradation Characteristics of Styrene-Butadiene-Styrene Copolymer Asphalt Binder Filled with an Inorganic Flame-Retarding Agent

Asphalt binder is a complex mixture of dark brown polymers composed of hydrocarbons with generally poor fire resistance. To improve its flame retardancy when used in tunnel asphalt pavements, a new inorganic flame-retardant filler (FR) containing magnesium hydroxide, aluminum hydroxide, inorganic phosphate, and melamine salt was explored. Thereafter, limiting oxygen index (LOI) and smoke suppression tests for the flame-retarded asphalt binder (FRA) mastics mixed with FR and styrene-butadiene-styrene (SBS) copolymer asphalt binder were conducted. Thermogravimetric (TG) and differential scanning calorimetry (DSC) curves for the FRA were correspondingly generated. Based on the TG data, the reaction function g(α), apparent activation energy E_a, and pre-exponential factor A were quantitatively evaluated using kinetic analysis. In addition, a Fourier transform infrared spectrometry (FTIR) test was utilized to assess the effects of the presence of FR on the chemical composition of the asphalt binder. Dynamic shear rheometer (DSR) tests were also performed to evaluate the rheological behavior of FRA. Results show that the presence of the FR significantly reduced the LOI and improved the smoke suppression during combustion of the asphalt binder mastics. The presence of FR was found to increase the E_a and the complexity of the combustion reaction, thereby improving the flame retardancy of the asphalt binder. FTIR analysis indicated that the presence of FR did not induce any strong chemical reactions to significantly impact or alter the functional groups of the asphalt binder. Furthermore, it was also observed that the rutting parameter and critical failure temperature of FRA increased with the addition of FR due to the stiffening effect of the solid FR particles.

Resilience Analysis of Container Port Shipping Network Structure: The Case of China

The increased port outages caused by events such as war and public health emergencies have motivated the study of container port shipping network (CPSN) resilience. This paper proposes a resilience framework, which includes prevention, resistance, restoration, adaption, and optimization. The framework is used to analyze the resilience of the CPSN by detecting changing performance of the network indicators before and after the random attack or one of the deliberate attacks. The indicators include the network resilience index, degree distribution, independent path, cluster coefficient, network efficiency and connectivity. The comparative analysis is based on the statistics of China’s cases in 2005 and 2017. The results indicate that, first, the resilience of the structure of China’s container port shipping network (CCPSN) in 2017 has improved when comparing the 2015 situation. Second, the performance of indicators under betweenness attack (BA) decreases faster than other attacks; the resilience index of deliberate attacks is poorer, when compared with the random attack (RA). Third, network resilience can be improved by protecting and adding hub port nodes. Priority should be given to restoring the hub port nodes during the recovery process. The same network indicator recovers similarly after facing different attacks, while different indicator shows various recovery process. Thus, it is necessary to consider the different recovery performances of network indicators when the damaged CPSN selects recovery mode.

A Markov Decision Process Approach for Cost-Benefit Analysis of Infrastructure Resilience Upgrades

As climate change threatens to cause increasingly frequent and severe natural disasters, decisionmakers must consider costly investments to enhance the resilience of critical infrastructures. Evaluating these potential resilience improvements using traditional cost-benefit analysis (CBA) approaches is often problematic because disasters are stochastic and can destroy even hardened infrastructure, meaning that the lifetimes of investments are themselves uncertain. In this article, we develop a novel Markov decision process (MDP) model for CBA of infrastructure resilience upgrades that offer prevention (reduce the probability of a disaster) and/or protection (mitigate the cost of a disaster) benefits. Stochastic features of the model include disaster occurrences and whether or not a disaster terminates the effective life of an earlier resilience upgrade. From our MDP model, we derive analytical expressions for the decisionmaker's willingness to pay (WTP) to enhance infrastructure resilience, and conduct a comparative static analysis to investigate how the WTP varies with the fundamental parameters of the problem. Following this theoretical portion of the article, we demonstrate the applicability of our MDP framework to real-world decision making by applying it to two case studies of electric utility infrastructure hardening. The first case study considers elevating a flood-prone substation and the second assesses upgrading transmission structures to withstand high winds. Results from these two case studies show that assumptions about the value of lost load during power outages and the distribution of customer types significantly influence the WTP for the resilience upgrades and are material to the decisions of whether or not to implement them.

Resilience in Infrastructure Systems: A Comprehensive Review

When encountering crisis events, systems, organizations, and people must react and handle these unpredictable events. Under these circumstances, important social functions and infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Although considerable research has been conducted on the resilience of infrastructure systems over the past years, a critical review of these studies remains lacking. Therefore, this study aims to bridge the knowledge gap by presenting a comprehensive review of infrastructure research conducted in the past decade, namely, from 2011 to 2021. On the basis of a systematic search, this study identified 222 journal articles investigating infrastructure resilience. A review of the identified papers revealed five research streams in the area of infrastructure resilience (IR), namely, the assessment of infrastructure resilience, improvement of infrastructure resilience, conceptualizing infrastructure resilience from various perspectives, factors influencing infrastructure resilience, and the prediction of infrastructure resilience. This study also presented some directions that future research can pursue. These directions include analyzing factors influencing infrastructure resilience based on simulation, assessing the resilience of green infrastructure, improving the resilience of interdependent infrastructure, and predicting the resilience of infrastructure based on empirical research.

The Importance of Risk Management: What is Missing in ISO Standards?

The overall aim of this article is to contribute to the further development of the area of risk analysis and risk management in the International Organization for for Standardization (ISO) standards by strengthening its scientific basis. Industrial standards, especially ISO standards, are the tools organizations use to manage their risk, through following their guidance and complying with their requirements. Organizations confirm their compliance with these standards through certification, which means that they heavily depend upon the quality of the ISO standards to enable them to effectively manage their risk. The purpose of this study is to investigate what guidance is given on key elements of risk management and how well ISO standards are aligned with state-of-the-art risk management literature. Eighteen ISO standards, all addressing risk management, were reviewed in this study with regard to risk terminology and guidance. The results of the study confirm the increasing importance of risk management for business. However, the study also shows a lack of guidance on doing risk analysis in the industrial standards examined. The ISO management system standards and guidelines are not aligned with the scientific literature on risk and are not appropriate for the management of risk arising from complex interactions and emergent behavior that is inherent in present-day sociotechnical systems.

Integrating disaster management planning into road infrastructure asset management

Climate change presents challenges for road infrastructure asset managers, with uncertainty about the impacts of increasingly frequent and intense disasters on asset functionality and lifespan. Budgeting for disaster response and recovery is improving; however, there are calls for more evidence to support budget allocations. This exploratory study aimed to create a systematic approach for integrating climate-related disaster risks within road asset evaluation, towards improving the evidence-base for capital works and maintenance budgets. It involved a systematic literature review of 63 research papers about resilient road infrastructure, followed by sense-checking key findings through eight semi-structured interviews with road asset management experts from the Australian government and industry. The authors present a set of 15 parameters for evaluating road infrastructure resilience, within four commonly used asset management categories: robustness, redundancy, resourcefulness and rapidity. These are placed within a disaster management planning framework for resilient road infrastructure outcomes, with the key parameters matched to the commonly used disaster management phases of ‘Prevent, Prepare, Respond, Recover’. The parameters and the framework have immediate use for road infrastructure asset managers, providing clear decision-support regarding what data are important to collect and analyse and at what time, to inform capital works and maintenance budgets.

An experimental investigation of resilience decision making in repeated disasters

Given the growing prevalence of catastrophic events and health epidemics, policymakers are increasingly searching for effective strategies to encourage firms to invest in resilience rather than relying on insurance or government assistance. Too often, however, resilience research focuses on decisions made by firms and emergency planners in the context of "one-off" events. We extend this research by examining resilience decision making in the more realistic context of repeated catastrophic events. Using a population of professional managers of middle market firms and a university experimental economics subject pool, we conduct a series of controlled experiments on the decision to invest in inventories to improve firm resilience to repeated catastrophic events. While existing economic and supply chain resilience research has focused on resilience in terms of avoiding some magnitude of economic losses, existing research omits a focus on the probability of those losses. Controlled experiments can evaluate the influence of probability more effectively than observational data by better controlling for magnitude and more easily accounting for repeated events. We find that decision makers are less likely to make resilience investments when a disaster has recently occurred. We further find that advisory information alone is insufficient to motivate resilience investments by firms. It must be substantiated by a history of advisory accuracy. However, we find that this effect is heavily moderated by the type of advisory information provided; we find that firm managers are much more likely to trust precautionary advice.

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors' accomplishments, and proposes 10 major risk-related challenges for 2020-2030. Authors conclude that the next decade will severely test the field of risk analysis.

Human-centric infrastructure resilience: Uncovering well-being risk disparity due to infrastructure disruptions in disasters

The objective of this paper is to empirically examine the impacts of infrastructure service disruptions on the well-being of vulnerable populations during disasters. There are limited studies that empirically evaluate the extent to which disruptions in infrastructure system services impact subpopulation groups differently and how these impacts relate to the wellbeing of households. Being able to systematically capture the differential experiences of sub-populations in a community due to infrastructure disruptions is necessary to highlight the differential needs and inequities that households have. In order to address this knowledge gap, this study derives an empirical relationship between sociodemographic factors of households and their subjective well-being impacts due to disruptions in various infrastructure services during and immediately after Hurricane Harvey. Statistical analysis driven by spearman-rank order correlations and fisher-z tests indicated significant disparities in well-being due to service disruptions among vulnerable population groups. The characterization of subjective well-being is used to explain to what extent infrastructure service disruptions influence different subpopulations. The results show that: (1) disruptions in transportation, solid waste, food, and water infrastructure services resulted in more significant well-being impact disparities as compared to electricity and communication services; (2) households identifying as Black and African American experienced well-being impact due to disruptions in food, transportation, and solid waste services; and (3) households were more likely to feel helpless, difficulty doing daily tasks and feeling distance from their community as a result of service disruptions. The findings present novel insights into understanding the role of infrastructure resilience in household well-being and highlights why it is so important to use approaches that consider various factors. Infrastructure resilience models tend to be monolithic. The results provide empirical and quantitative evidence of the inequalities in well-being impacts across various sub-populations. The research approach and findings enable a paradigm shift towards a more human-centric approach to infrastructure resilience.

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Purpose

As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.

Design/methodology/approach

A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.

Findings

The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.

Originality/value

In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

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

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.

Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower-level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower-level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin-destination pair, eventually converging to the user equilibrium traffic assignment. The upper-level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near-optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two-layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.

Resilience-Based Recovery Assessments of Networked Infrastructure Systems under Localized Attacks

To reduce unforeseen disaster risks, infrastructure systems are expected to be resilient. The impact of many natural disasters on networked infrastructures is often observed to follow a localized attack pattern. The localized attack can be demonstrated by the failures of a group of links concentrated in a particular geographical domain which result in adjacent isolated nodes. In this paper, a resilience-based recovery assessment framework is proposed. The framework aims to find the most effective recovery strategy when subjected to localized attacks. The proposed framework was implemented in a lattice network structure inspired by a water distribution network case study. Three different recovery strategies were studied with cost and time constraints incorporated: preferential recovery based on nodal weight (PRNW), periphery recovery (PR), and localized recovery (LR). The case study results indicated that LR could be selected as the most resilient and cost-effective recovery strategy. This paper hopes to aid in the decision-making process by providing a strategic baseline for finding an optimized recovery strategy for localized attack scenarios.

Critical Infrastructure Vulnerability to Spatially Localized Failures with Applications to Chinese Railway System

This article studies a general type of initiating events in critical infrastructures, called spatially localized failures (SLFs), which are defined as the failure of a set of infrastructure components distributed in a spatially localized area due to damage sustained, while other components outside the area do not directly fail. These failures can be regarded as a special type of intentional attack, such as bomb or explosive assault, or a generalized modeling of the impact of localized natural hazards on large-scale systems. This article introduces three SLFs models: node centered SLFs, district-based SLFs, and circle-shaped SLFs, and proposes a SLFs-induced vulnerability analysis method from three aspects: identification of critical locations, comparisons of infrastructure vulnerability to random failures, topologically localized failures and SLFs, and quantification of infrastructure information value. The proposed SLFs-induced vulnerability analysis method is finally applied to the Chinese railway system and can be also easily adapted to analyze other critical infrastructures for valuable protection suggestions.

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

In crisis situations, systems, organizations, and people must react and deal with events that are inherently unpredictable before they occur: vital societal functions and thus infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Progress concerning its conceptualization has been made but it remains difficult to assess and apply in practice. The results of this article stem from a literature review allowing the analysis of current advances in the development of proposals to improve the management of infrastructure resilience. The article: (i) identifies different dimensions of resilience; (ii) highlights current limits of assessing and controlling resilience; and (iii) proposes several directions for future research that could go beyond the current limits of resilience management, but subject to compliance with a number of constraints. These constraints are taking into account different hazards, cascade effects, and uncertain conditions, dealing with technical, organizational, economical, and human domains, and integrating temporal and spatial aspects.

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

Resilience is the capability of a system to adjust its functionality during a disturbance or perturbation. The present work attempts to quantify resilience as a function of reliability, vulnerability, and maintainability. The approach assesses proactive and reactive defense mechanisms along with operational factors to respond to unwanted disturbances and perturbation. This article employs a Bayesian network format to build a resilience model. The application of the model is tested on hydrocarbon-release scenarios during an offloading operation in a remote and harsh environment. The model identifies requirements for robust recovery and adaptability during an unplanned scenario related to a hydrocarbon release. This study attempts to relate the resilience capacity of a system to the system's absorptive, adaptive, and restorative capacities. These factors influence predisaster and postdisaster strategies that can be mapped to enhance the resilience of the system.

A Systems-Based Risk Assessment Framework for Intentional Electromagnetic Interference (IEMI) on Critical Infrastructures

Modern infrastructures are becoming increasingly dependent on electronic systems, leaving them more vulnerable to electrical surges or electromagnetic interference. Electromagnetic disturbances appear in nature, e.g., lightning and solar wind; however, they may also be generated by man-made technology to maliciously damage or disturb electronic equipment. This article presents a systematic risk assessment framework for identifying possible, consequential, and plausible intentional electromagnetic interference (IEMI) attacks on an arbitrary distribution network infrastructure. In the absence of available data on IEMI occurrences, we find that a systems-based risk assessment is more useful than a probabilistic approach. We therefore modify the often applied definition of risk, i.e., a set of triplets containing scenario, probability, and consequence, to a set of quadruplets: scenario, resource requirements, plausibility, and consequence. Probability is "replaced" by resource requirements and plausibility, where the former is the minimum amount and type of equipment necessary to successfully carry out an attack scenario and the latter is a subjective assessment of the extent of the existence of attackers who possess the motivation, knowledge, and resources necessary to carry out the scenario. We apply the concept of intrusion areas and classify electromagnetic source technology according to key attributes. Worst-case scenarios are identified for different quantities of attacker resources. The most plausible and consequential of these are deemed the most important scenarios and should provide useful decision support in a countermeasures effort. Finally, an example of the proposed risk assessment framework, based on notional data, is provided on a hypothetical water distribution network.

Stability of a giant connected component in a complex network

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

Resilience and efficiency in transportation networks

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.