Resilience and efficiency in transportation networks

Power law of path multiplicity in complex networks

Complex networks describe a wide range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a vital role in network science. Rather than focusing on the path length or path centrality, here we draw attention to the path multiplicity related to decision-making efficiency, which is defined as the number of shortest paths between node pairs and thus characterizes the routing choice diversity. Notably, through extensive empirical investigations from this new perspective, we surprisingly observe a "hesitant-world" feature along with the "small-world" feature and find a universal power-law of the path multiplicity, meaning that a small number of node pairs possess high path multiplicity. We demonstrate that the power-law of path multiplicity is much stronger than the power-law of node degree, which is known as the scale-free property. Then, we show that these phenomena cannot be captured by existing classical network models. Furthermore, we explore the relationship between the path multiplicity and existing typical network metrics, such as average shortest path length, clustering coefficient, assortativity coefficient, and node centralities. We demonstrate that the path multiplicity is a distinctive network metric. These results expand our knowledge of network structure and provide a novel viewpoint for network design and optimization with significant potential applications in biological, social, and man-made networks.

Goldilocks Fluctuations: Dynamic Constraints on Loop Formation in Scale-Free Transport Networks

Adaptive transport networks are known to contain loops when subject to hydrodynamic fluctuations. However, fluctuations are no guarantee that a loop will form, as shown by loop-free networks driven by oscillating flows. We provide a complete stability analysis of the dynamical behavior of any loop formed by fluctuating flows. We find a threshold for loop stability that involves an interplay of geometric constraints and hydrodynamic forcing mapped to constant and fluctuating components. Loops require fluctuation in the relative size of the flux between nodes, not just a temporal variation in the flux at a given node. Hence, there is both a minimum and a maximum amount of fluctuation relative to the constant-flux component where loops are supported.

Synergistic evolution of water-energy-food system resilience and efficiency in urban agglomerations

With increasing internal and external risks to the WEF system, a single emphasis on efficiency or a lopsided pursuit of resilience can lead to difficulties in adapting to complex changes and resource redundancy. Revealing the synergistic evolutionary characteristics between efficiency and resilience of the WEF system is an effective method to deal with systemic internal and external risks. However, the current study of the WEF system lacks a synergistic perspective on resilience and efficiency. Thus, taking Chengdu-Chongqing Economic Circle (CCEC) as the research object and its geospatial boundary as the system boundary, this paper adopted the entropy-topsis model to evaluate the WEF resilience, and applied the super-efficient SBM model to measure the WEF efficiency accurately, which fully considered the non-expected outputs in the process of resource utilization. Then, applying the development coordination degree model, the synergistic relationship between the two was measured. The results indicated that: the average value of WEF resilience in CCEC increased from 0.414 to 0.485 and showed spatial characteristics of west＞east＞central. The WEF efficiency interval was 0.79-0.93, and cities with average WEF efficiency reaching the effective production frontier accounted for only 37.5%. The clustered distribution of the synergy levels intensified. The number of cities with primary, medium, more advanced, and advanced levels was 6, 6, 1, and 3, respectively, with primary and medium synergy levels dominating. The findings suggest that cities should strengthen regional exchanges and formulate targeted measures based on their own situations. In addition, CCEC should possess a comprehensive understanding of the interdependencies and conflicts that arise between resilience and efficiency throughout the decision-making procedure.

Resilience of transportation infrastructure networks to road failures

Anthropogenic climate change drives extreme weather events, leading to significant consequences for both society and the environment. This includes damage to road infrastructure, causing disruptions in transportation, obstructing access to emergency services, and hindering humanitarian organizations after natural disasters. In this study, we develop a novel method for analyzing the impacts of natural hazards on transportation networks rooted in the gravity model of travel, offering a fresh perspective to assess the repercussions of natural hazards on transportation network stability. Applying this approach to the Ahr valley flood of 2021, we discovered that the destruction of bridges and roads caused major bottlenecks, affecting areas considerably distant from the flood's epicenter. Furthermore, the flood-induced damage to the infrastructure also increased the response time of emergency vehicles, severely impeding the accessibility of emergency services. Our findings highlight the need for targeted road repair and reinforcement, with a focus on maintaining traffic flow for emergency responses. This research provides a new perspective that can aid in prioritizing transportation network resilience measures to reduce the economic and social costs of future extreme weather events.

Resilience patterns of urban road networks under the worst-case localized disruptions

Recent events, including COVID-19, extreme floods, and explosion accidents, commonly induced localized closures and disruptions of urban road networks (URNs), resulting in significant impacts on human mobility and socio-economic activities. Existing studies on URN resilience to those events mainly took few cases for empirical studies, limiting our understanding on the URN resilience patterns across different cities. By conducting a large-scale nationwide resilience analysis of URNs in 363 cities in mainland China, this study attempts to uncover the resilience patterns of URNs against the worst-case single (SLDs) and multiple localized disruptions (MLDs). Results show that the distance from the worst-case SLD to the city center would be less than 5 km in 62.3% cities, as opposed to more than 15 km in 14.3% cities. Moreover, the average road network resilience of cities in western China could be 7% and 13% smaller than that of the eastern cities under the worst-case SLDs and MLDs, respectively. This inequality in the worst-case resilience is partly attributable to variations in urban socio-economic, infrastructure-related, and topographic factors. These findings could inspire nationwide pre-disaster mitigation strategies to cope with localized disruptions and help transfer insights for mitigation strategies against disruptive events across cities.

Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility

The growing interconnectedness of urban infrastructure networks presents challenges to their ability to handle unforeseen disruptions, particularly in the context of extreme weather events resulting from climate change. Understanding the resilience of interconnected infrastructure systems is imperative to effectively manage such disruptions. This study investigates the role of interconnectedness in enhancing the resilience of public transportation systems in Hong Kong, a city heavily reliant on public transit. Our results demonstrate that interconnected transportation systems improve resilience by reducing topological vulnerabilities, increasing attack tolerance, and enhancing post-disruption interoperability. Findings also identify the potential to integrate vulnerable systems for greater robustness and highlight the marginal benefits of enhancing intermodal transfer. Strengthening interconnectedness among modes of urban public transit fosters a safe-to-fail system, presenting a distinct resilience-by-design approach. This complements conventional resilience-by-intervention approaches that focus on improving individual systems or introducing entirely new systems.

Urban resilience under the COVID-19 pandemic: A quantitative assessment framework based on system dynamics

The COVID-19 pandemic, which lasted for three years, has had a great impact on the public health system, society and economy of cities, revealing the insufficiency of urban resilience under large-scale public health events (PHEs). Given that a city is a networked and multidimensional system with complex interactions, it is helpful to improve urban resilience under PHEs based on system thinking. Therefore, this paper proposes a dynamic and systematic urban resilience framework that incorporates four subsystems (governance, infrastructures, socioeconomy and energy-material flows). The composite index, system dynamics and epidemic simulation model are integrated into the framework to show the nonlinear relationships in the urban system and reflect the changing trend of urban resilience under PHEs. Then, urban resilience under different epidemic scenarios and response policy scenarios is calculated and discussed to provide some suggestions for decision-makers when faced with the trade-off between the control of PHEs and the maintenance of city operation. The paper concludes that control policies could be adjusted according to the characteristics of PHEs; strict control policies under a severe epidemic could lead to a significant decrease in urban resilience, while a more flexible control strategy can be adopted under a mild epidemic scenario to ensure the normal operation of urban functions. Moreover, the critical functions and impact factors of each subsystem are identified.

Analysing dynamic work systems using DynEAST: a demonstration of concept

The capability of current Ergonomics methods to capture dynamism is limited, stifling our understanding of work-as-done, distributed situational awareness and organisational drift. This paper provides a demonstration of concept of DynEAST; an extension of the EAST framework underpinned by principles from Dynamic Network Analysis, to capture elements of dynamism within work systems. The DynEAST concept is applied to a railway maintenance case study. Case study findings demonstrate how DynEAST outputs can be used to advance our understanding of the aforementioned phenomena and better equip practitioners for current and future Ergonomics challenges.Practitioner summary: This paper introduces the DynEAST method. DynEAST enables HF/E practitioners to model and analyse dynamic features of complex work systems. The development of DynEAST is timely due to the concurrent proliferation of increasingly complex sociotechnical systems and stagnation of HF/E methods development; particularly those able to model systemic dynamism. Abbreviations: DynEAST: dynamic event analysis of systemic teamwork; EAST: dynamic event analysis of systemic teamwork; HF/E: human factors and ergonomics; HF: human factors; DNA: dynamic network analysis; HTA: hierarchal task analysis; CWA: cognitive work analysis; CAST: causal analysis based on system theory; STAMP: system theoretic accident model and processes; FRAM: functional resonance analysis method; SNA: social network analysis; DSA: distributed situational awareness; PPO: possession protection officer; PO: protection officer; RTS: railway track signals; LPA: local possession authority; SMEs: subject matter experts.

Recursive traffic percolation on urban transportation systems

This paper proposes a recursive traffic percolation framework to capture the dynamics of cascading failures and analyze potential overloaded bottlenecks. In particular, compared to current work, the influence of external flow is considered, providing a new perspective for the study of regional commuting. Finally, we present an empirical study to verify the accuracy and effectiveness of our framework. Further analysis indicates that external flows from different regions affect the network. Our work requires only primary data and verifies the improvement of the functional network.

Lessons on employees' digital resilience from COVID-19-induced transitions to remote work – a mixed methods study

Purpose

In response to the COVID-19 pandemic, many employees had to switch to remote work. While some adjusted successfully to this transition, others have struggled. Leveraging information systems (IS) to adjust to major exogenous shocks is called digital resilience. The purpose of this paper is to understand what we can learn about employees' digital resilience from externally enforced transitions to remote work.

Design/methodology/approach

As digital resilience is challenging to measure, this study uses an embedded mixed methods approach. The authors conducted a qualitative analysis of 40 employees' statements on their remote work experience during the first six months of the pandemic and complemented these findings with scale-based digital resilience scores.

Findings

The authors find that employees' digital resilience largely depends on the amount of technical equipment and support they receive from their organizations as well as their ability and willingness to learn how to adequately use and communicate through information and communication technologies. Being self-disciplined and self-responsible positively affects digital resilience, while social isolation threatens it. Organizations can foster digital resilience building by encouraging digital networking, building a digital culture and netiquette, and treating digital resilience as a sociotechnical phenomenon.

Originality/value

This is one of the first empirical studies of digital resilience on a human level. It sheds light on the missing link between IS-enabled resilience and transitions to remote work. Specifically, it provides original insights into its development and manifestation in a remote work context during the COVID-19 pandemic. For researchers, it provides novel guidance on choosing appropriate measurement instruments to capture digital resilience.

Synergistic relationship or not? Understanding the resilience and efficiency of the tourism economy: evidence from Hainan Province, China

The COVID-19 pandemic has dealt a serious blow to the global tourism industry, causing a fracturing of and decline in tourism development efficiency and even a stagnation of tourism development in some regions. To solve the contradiction between efficiency and quality, it is necessary to ensure the endogenous power of tourism resilience while pursuing the efficiency of tourism development. This study assumes that Hainan Province follows a tourism development path led by resilience. The improved weighting method, EBM model and Haken model are used to evaluate the level of resilience, the level of efficiency and their co-evolution. The findings indicate that the core tourism cities represented by Sanya and Haikou have a high level in the individual fields of tourism development efficiency and tourism economic resilience but have limited performance in the synergistic relationship between tourism development efficiency and tourism economic resilience. In contrast, the marginal tourism cities represented by Tunchang County and Ledong County have low tourism development efficiency and resilience, but their synergistic development level is high. This result proves that co-evolution plays a dual forward and reverse driving role. Based on the identification of the order parameters, it is concluded that Hainan Province is characterized by a synergistic evolutionary synergy dominated by resilience, which is in line with the trend of social development and the sustainable development of tourism. While reasonably pursuing the tourism economy and development efficiency, we should pay attention to strengthening resilience construction based on multiple aspects, such as tourists, enterprises, organizations, governments and destinations.

A link model approach to identify congestion hotspots

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future.

The Impact of Climate Change on Urban Transportation Resilience to Compound Extreme Events

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

Urban transport infrastructures (TIs) play a central role in an urban society that faces more and more disasters. TIs, part of critical infrastructures (CIs), are highly correlated with urban disaster management in terms of their resilience when cities are facing a crisis or disaster. According to many studies, indicator assessment has been frequently used for the resilience management of CIs in recent decades. Defining and characterizing indicators can be useful for disaster managers as it could help monitor and improve the capacities and performance of TIs. The purpose of this paper, therefore, is (1) to identify and summarize the existing indicators of TIs resilience from the currently available literature, and (2) to discuss the possible future studies of the resilience indicator of TIs. The first results indicated that there are some barriers to identify indicators following the common search method through keywords. Additionally, the indicators found are mainly related to technical information, the disruption stage, and internal TIs. Finally, due to the complexity of indicator assessment, sub-indicators and indicator spatialization are widely used in the resilience assessment of urban TIs studies.

Local impacts on road networks and access to critical locations during extreme floods

Floods affected more than 2 billion people worldwide from 1998 to 2017 and their occurrence is expected to increase due to climate warming, population growth and rapid urbanization. Recent approaches for understanding the resilience of transportation networks when facing floods mostly use the framework of percolation but we show here on a realistic high-resolution flood simulation that it is inadequate. Indeed, the giant connected component is not relevant and instead, we propose to partition the road network in terms of accessibility of local towns and define new measures that characterize the impact of the flooding event. Our analysis allows to identify cities that will be pivotal during the flooding by providing to a large number of individuals critical services such as hospitalization services, food supply, etc. This approach is particularly relevant for practical risk management and will help decision makers for allocating resources in space and time.

Factors affecting the spatial resilience of Ethiopia's secondary cities to urban uncertainties: A study of household perceptions of Kombolcha city

The resilience measurement focuses on urban shocks and stresses, which are excluded from current spatial resilience assessments. As a result, existing literature suggests that research in secondary cities of the global south is needed to understand better spatial resilience in the face of multivariate, intersecting, and uncertain challenges. This study aims to determine the factors affecting the spatial resilience of Ethiopia's secondary cities to urban uncertainties using household perceptions of Kombolcha city. The study collected empirical data through questionnaires and key informant interviews, and then analyzed those using SPSS and the Analytic Hierarchy Process. Accordingly, seventeen environmental and physical urban problems affecting the spatial resilience of the country's secondary cities were identified. Deforestation, surface flooding, landslides, poor solid waste management, and inadequate drainage facilities were perceived as top priority urban problems in Kombolcha city with the respective values of 19.73%, 13.02%, 12.70%, 7.59%, and 6.82% of the four hundred sampled households. However, water scarcity and wind-related shocks, scoring 1.48% and 1.89%, respectively, were the least recurring urban problems. The city's spatial resilience is further limited by unsustainable material and resource consumption, a lack of infrastructure, poor transportation system conditions, poor implementation of response measures: lack of appropriate planning, and non-long-lasting biophysical measures. The household perception also showed that the urban uncertainties are severe in the city, with a 49.48% response rate. The findings also revealed a relationship and commonalities amongst the problems exacerbated by land-use zoning changes and the thriving informal settlements. The study implied that improving secondary cities' coping, adaptation, and governance systems are critical for mitigating the perceived urban problems and making cities spatially resilient. Thus, the study's spatial planning implications are that local governments in secondary cities commit to localizing international initiatives, strictly establishing and enforcing local resource utilization strategies, and improving living conditions in their cities.

Dynamical efficiency for multimodal time-varying transportation networks

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.

Change in Urban Land Use Efficiency in China: Does the High-Speed Rail Make a Difference?

High-speed rail (HSR) increases the non-local connections in cities and plays an essential role in urban land use efficiency. This paper uses a multi-period difference-in-difference model and a threshold model based on sample data that cover 284 Chinese cities from 2003–2018 to investigate the impact of HSR on urban land use efficiency. The results show that there is a 0.021 increase in urban land use efficiency after opening the HSR. The number of HSR stations and routes can increase the urban land use efficiency by 0.004 and 0.013, respectively. Compared with the cities in the East, the midwestern ones are more vulnerable to the impact of HSR. In particular, the positive impact of the number of HSR stations on the urban land use efficiency in cities with an urban population density exceeding 795 person/km² is two times larger than cities with an urban population density of less than 795 person/km². In addition, the impact of the number of HSR routes on urban land use efficiency in cities with an urban population density of less than 1003 person/km² is five times larger than that of cities with an urban population density exceeding 1003 person/km².

Climate effects on US infrastructure: the economics of adaptation for rail, roads, and coastal development

Changes in temperature, precipitation, sea level, and coastal storms will likely increase the vulnerability of infrastructure across the USA. Using models that analyze vulnerability, impacts, and adaptation, this paper estimates impacts to railroad, roads, and coastal properties under three infrastructure management response scenarios: No Adaptation; Reactive Adaptation, and Proactive Adaptation. Comparing damages under each of these potential responses provides strong support for facilitating effective adaptation in these three sectors. Under a high greenhouse gas emissions scenario and without adaptation, overall costs are projected to range in the $100s of billions annually by the end of this century. The first (reactive) tier of adaptation action, however, reduces costs by a factor of 10, and the second (proactive) tier reduces total costs across all three sectors to the low $10s of billions annually. For the rail and road sectors, estimated costs for Reactive and Proactive Adaptation scenarios capture a broader share of potential impacts, including selected indirect costs to rail and road users, and so are consistently about a factor of 2 higher than prior estimates. The results highlight the importance of considering climate risks in infrastructure planning and management.

Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks

Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.

Infrastructure networks are characterized by fluctuations of flow demand between different points and temporal congestion or overload on flow pathways. Hamedmoghadam et al. identify congestion bottlenecks in networks relevant to communication, transportation, water supply, and power distribution.

Assessing the interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting

BACKGROUND

The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use.

FINDINGS

While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread.

SCOPE

Our results suggest that the declaration of micro-containment zones within the city with high road network density combined with enhanced testing can help in containing the outbreaks until clinical interventions become available.

Monitoring of transport infrastructure exposed to multiple hazards: a roadmap for building resilience

Monitoring-enhanced resilience in transport management is emerging together with the new technologies and digital data, however have not been fully explored yet. Digital technologies have the potential to provide rapid resilience assessments in a quantifiable and engineered manner for transport infrastructure, which is exposed to multiple natural and human-induced hazards and diverse loads throughout their life-cycle. Physical damage and disruption of networks and interdependent systems may cause tremendous socioeconomic impact, affecting world economies and societies. Nowadays, transport infrastructure stakeholders have shifted the requirements in risk and resilience assessment. The expectation is that risk is estimated efficiently, almost in real-time with high accuracy, aiming at maximising the functionality and minimising losses. Nevertheless, no integrated framework exists for quantifying resilience to diverse hazards, based on structural and functionality monitoring (SHFM) data, and this is the main capability gap that this paper envisages filling. Monitoring systems have been used widely in transport infrastructure and have been studied extensively in the literature. Data can facilitate prognosis of the asset condition and the functionality of the network, informing computer-based asset and traffic models, which can assist in defining actionable performance indicators, for diagnosis and for defining risk and loss expediently and accurately. Evidence exists that SHFM is an enabler of resilience. However, strategies are absent in support of monitoring-based resilience assessment in transport infrastructure management. In response to the above challenge, this paper puts forward for the first time in the international literature, a roadmap for monitoring-based quantification of resilience for transport infrastructure, based on a comprehensive review of the current state-of-the-art. It is a holistic asset management roadmap, which identifies the interactions among the design, monitoring, risk assessment and quantification of resilience to multiple hazards. Monitoring is embraced as a vital component, providing expedient feedback for recovery measures, accelerating decision-making for adaptation of changing ecosystems and built environments, utilising emerging technologies, to continuously deliver safer and resilient transport infrastructure.

Discontinuous transition to loop formation in optimal supply networks

The structure and design of optimal supply networks is an important topic in complex networks research. A fundamental trait of natural and man-made networks is the emergence of loops and the trade-off governing their formation: adding redundant edges to supply networks is costly, yet beneficial for resilience. Loops typically form when costs for new edges are small or inputs uncertain. Here, we shed further light on the transition to loop formation. We demonstrate that loops emerge discontinuously when decreasing the costs for new edges for both an edge-damage model and a fluctuating sink model. Mathematically, new loops are shown to form through a saddle-node bifurcation. Our analysis allows to heuristically predict the location and cost where the first loop emerges. Finally, we unveil an intimate relationship among betweenness measures and optimal tree networks. Our results can be used to understand the evolution of loop formation in real-world biological networks.

Supply networks with optimal structure do not contain loops but these can arise as a result of damages or fluctuations. Here Kaiser et al. uncover the mechanisms of loop formation, predict their location and draw analogies with loop formation in biological networks such as plants and animal vasculature.

The Impact of COVID-19 Pandemic on the Resilience of Sustainable Mobility in Sicily

The COVID-19 pandemic has resulted in unprecedented measures changing travel habits in many countries. Many users have started to prefer traveling by private cars, which is against the sustainability policies of the European cities. The necessity of gaining a deeper understanding of road users’ travel habit changes, their feelings on public transport use, and their perceptions of using sustainable urban mobility modes has emerged for future transport planning. Considering these facts, the study in this paper aimed to investigate the influence of the COVID-19 pandemic on road users’ perceptions, needs, and use of sustainable travel modes (i.e., public transport, walking, and cycling). An online survey was carried out during the period from March to May 2020 in the case study area, Sicily of Southern Italy. Regarding the population of the case study, the survey was representative, with 431 individuals. The survey included variables, namely gender, age, city of residence, private car ownership, walking and cycling frequency before and during the pandemic, public transport use frequency for leisure activities before and during the pandemic, need for remote working, and the stress and anxiety perception of using public transport during the pandemic. The analysis started with descriptive statistics and it was followed by correlation analysis in order to explore the characteristics of the dataset and relationship between variables. It was found that these were not statistically significantly correlated at a 95% confidence level. An ordinal regression model was applied for determining the predictions. The results suggested that women were less likely to walk during the pandemic than men. Participants were more likely to resume remote work even after the second phase in order to reduce their daily travel needs and keep their isolation. Participants have expressed a positive opinion on the use of micromobility during pandemic situations. These results can be considered as a basis for sustainable urban planning and a guide for decision-makers who aim to encourage the use of public transport, walking, cycling, and micromobility.

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

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.

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

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.

Sustainability of Transport System of Large Russian City in the Period of COVID-19: Methods and Results of Assessment

In spring 2020 the whole world went through the “black swan”—COVID-19 pandemic. The healthcare systems of all countries and the world economy, in general, became very stressed. The extraordinary decline of activity in all spheres, except healthcare, led to a drop in the demand for transport services, including city public transport. It was important for city management to support the sustainability of the local transport system. The article presents fundamental approaches to assessing the sustainability of a transport service, particularly city passenger public transport (CPPT), for the example of the large Russian city Tyumen (size of population—nearly 807 thousand people). Methods of analysis of the sustainability of the transport process in conditions of negative environmental impact (COVID-19 pandemic) are considered. During the period from 30.03.2020 to 31.05.2020 (nine weeks—the acute phase of COVID-19 pandemic) structural sustainability of the CPPT system in Tyumen kept a high level. By changing the parameters of the planned characteristics of the CPPT system state, an attempt to adapt the transport service supply to a sharp decrease in transport demand was made. In the period of “self-isolation”, the demand for the CPPT transport service reduced more than the transport service supply. Sustainability of CPPT functioning was evaluated by calculating the elasticity of the transport supply (number of trips) in relation to the actual demand (actual volume of transportations). Calculation of the elasticity index of the CPPT system of Tyumen during nine weeks of April–May 2020 (duration of “self-isolation“) is provided. A conclusion was made from the results of the research. In particular, it was found that the foreground target function of city management was the maintenance of a high level of transportation processes to the detriment of the transportations’ effectiveness. Such a policy led to contradictory results—the additional financial expenses at the rate of 135–150 million rubles and quite a high level of contentment of the Tyumen population with the quality of the CPPT work (sociological research established that 80–85% of respondents were satisfied with the quality of the transport service in April–May 2020).

A network percolation-based contagion model of flood propagation and recession in urban road networks

In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristics-flood propagation rate ([Formula: see text]), flood incubation rate ([Formula: see text]), and recovery rate ([Formula: see text])-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks.

When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond

As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.

Evaluation of Spatial Resilience of Highway Networks in Response to Adverse Weather Conditions

Adverse weather poses a significant threat to the serviceability of highway infrastructure, as it causes more frequent and severe crash incidents. This study focuses on evaluating the resilience of highway networks by examining the crash-induced safety impact in response to extreme weather events. Unlike traditional service drop-based methods for resilience evaluation, this study endeavors to evaluate highway resilience in a spatial context. Three spatial metrics, including K-nearest neighbors, proximity to highways, and Kernel density hot spot, are introduced and employed to compare and analyze the spatial patterns (magnitude and distribution) of crashes in pre- and post-weather conditions. An illustrative example is also provided to showcase the applications of the proposed spatial resilience metrics for two study areas in the State of Illinois, U.S. The contribution of this study is to provide transportation practitioners with a tool to evaluate highway spatial resilience both visually and quantitatively, and ultimately improve highway safety and operation.

Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation

As demonstrated for extreme events, the resilience concept is used to evaluate the ability of a transportation system to resist and recover from disturbances. Motivated by the high cumulative impact of recurrent perturbations on transportation systems, we have investigated resilience quantification as a performance assessment method for high-probability low-impact (HPLI) disturbances such as traffic congestions. Resilience-based metrics are supplementary to conventional travel-time-based indices in literature. However, resilience is commonly quantified as a scalar variable despite its multi-dimensional nature. Accordingly, by hypothesizing increased information gain in performance assessment, we have investigated a multi-dimensional approach (mD-Resilience) for resilience quantification. Examining roadways’ resilience to recurrent congestions as a contributor to sustainable mobility, we proposed to measure resilience with several attributes that characterize the degradation stage, the recovery stage, and possible recovery paths. These attributes were integrated into a performance index by using Data Envelopment Analysis (DEA) as a non-parametric method. We demonstrated the increased information gain by quantifying the performance of major freeways in Los Angeles, California using Performance Measurement System (PeMS) data. The comparison of mD-Resilience approach with the method based on area under resilience curves showed its potential in distinguishing the severity of congestions. Furthermore, we showed that mD-Resilience also characterizes performance from the lens of delay and bottleneck severities.

Assessing resilience of healthcare infrastructure exposed to COVID-19: emerging risks, resilience indicators, interdependencies and international standards

In the moment of preparation of this paper, the world is still globally in grip of the Corona (COVID-19) crisis, and the need to understand the broader overall framework of the crisis increases. As in similar cases in the past, also with this one, the main interest is on the “first response”. Fully appreciating the efforts of those risking their lives facing pandemics, this paper tries to identify the main elements of the larger, possibly global, framework, supported by international standards, needed to deal with new (emerging) risks resulting from threats like Corona and assess the resilience of systems affected. The paper proposes that future solutions should include a number of new elements, related to both risk and resilience. That should include broadening the scope of attention, currently focused onto preparation and response phases, to the phases of “understanding risks”, including emerging risks, and transformation and adaptation. The paper suggests to use resilience indicators in this process. The proposed approach has been applied in different cases involving critical infrastructures in Europe (energy supply, water supply, transportation, etc., exposed to various threats), including the health system in Austria. The detailed, indicator-based, resilience analysis included mapping resilience, resilience stress-testing, visualization, etc., showing, already before the COVID-19, the resilience (stress-testing) limits of the infrastructures. A simpler (57 indicator based) analysis has, then been done for 11 countries (including Austria). The paper links these results with the options available in the area of policies, standards, guidelines and tools (such as the RiskRadar), with focus on interdependencies and global standards—especially the new ISO 31,050, linking emerging risks and resilience.

Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007–2016, while 94 were found in 2017–2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of “unknown unknowns” remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

Bouncing forward: a resilience approach to dealing with COVID-19 and future systemic shocks

Policy questions are often framed in popular discussion as situations where pulling the right levers will get the economy and society back on track after shocks and crises. This approach ignores how systems interact and how their systemic properties shape socioeconomic outcomes, leading to an over-emphasis on a limited set of characteristics, notably efficiency. We argue that this emphasis on efficiency in the operation, management and outcomes of various economic and social systems is not a conscious collective choice, but rather the response of the whole system to the incentives that individual components face. This has brought much of the world to rely upon complex, nested, and interconnected systems to deliver goods and services around the globe. While this approach has many benefits, the Covid-19 crisis shows how it has also reduced the resilience of key systems to shocks, and allowed failures to cascade from one system to others. This paper reviews the impact of COVID-19 on socioeconomic systems, discusses the notion of resilience, and provides specific recommendations on both integrating resilience analytics for recovery from the current crisis as well as on building resilient infrastructure to address future systemic challenges.

Non-Markovian recovery makes complex networks more resilient against large-scale failures

Non-Markovian spontaneous recovery processes with a time delay (memory) are ubiquitous in the real world. How does the non-Markovian characteristic affect failure propagation in complex networks? We consider failures due to internal causes at the nodal level and external failures due to an adverse environment, and develop a pair approximation analysis taking into account the two-node correlation. In general, a high failure stationary state can arise, corresponding to large-scale failures that can significantly compromise the functioning of the network. We uncover a striking phenomenon: memory associated with nodal recovery can counter-intuitively make the network more resilient against large-scale failures. In natural systems, the intrinsic non-Markovian characteristic of nodal recovery may thus be one reason for their resilience. In engineering design, incorporating certain non-Markovian features into the network may be beneficial to equipping it with a strong resilient capability to resist catastrophic failures.

Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China

Sustainability and its component resilience have become an important issue that cannot be neglected in airspace planning and development. Resilience, as an emerging system concept, is critical to sustainability in many fields. With the rapidly growing demand in China’s air transportation sector, airspace congestion and flight delays have become a major issue in the fast development of this sector, and threatens the sustainability and resilience of air traffic control (ATC) systems such as waste of resources, air pollution, etc. Sectors, the basic units of an ATC system, play a significant role in ensuring the safe and smooth operations of day-to-day flights. In this paper, we apply the complex network theory to establish a model of China’s air sector network (CASN) and examine a series of characteristic parameters with an empirical analysis on its vulnerability and resilience. Through a simulation-based approach, the CASN’s resilience was quantitatively assessed with a resilience indicator (RI) in different scenarios to identify the optimal recovery strategy for building higher system resilience. The results show that the CASN has a lengthy average shortest path and a small clustering coefficient, which demonstrates a hybrid topological feature. We have also found that betweenness has the greatest impact on the resilience and has managerial implications to understand the relationship between vulnerability and resilience in CASN, so as to achieve the resilience and sustainability of CASN.

Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007-2016, while 94 were found in 2017-2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of "unknown unknowns" remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

Robust component: a robustness measure that incorporates access to critical facilities under disruptions

The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

Local floods induce large-scale abrupt failures of road networks

The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods.

Scale-free resilience of real traffic jams

The concept of resilience can be realized in natural and engineering systems, representing the ability of a system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation systems, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations are still missing. Here, we define city traffic resilience based on the spatiotemporal clusters of congestion in real traffic and find that the resilience follows a scale-free distribution in 2D city road networks and 1D highways with different exponents but similar exponents on different days and in different cities. The traffic resilience is also revealed to have a scaling relation between the cluster size of the spatiotemporal jam and its recovery duration independent of microscopic details. Our findings of universal traffic resilience can provide an indication toward better understanding and designing of these complex engineering systems under internal and external disturbances.

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.

Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems

The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three-tier framework for risk assessment. We build on this structure to propose a tiered approach for resilience assessment that can be integrated into the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience are still lacking. Our proposed framework consists of tiers by which analysts can select resilience assessment and decision support tools to inform associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. The resilience management framework proposed is not intended to supplant either risk management or the many existing efforts of resilience quantification method development, but instead provide a guide to selecting tools that are appropriate for the given analytic need. The goal of this tiered approach is to intentionally parallel the tiered approach used in regulatory contexts so that resilience assessment might be more easily and quickly integrated into existing structures and with existing policies.