Link-level resilience analysis for real-world networks using crowd-sourced data

Towards a resilience assessment framework for the airport passenger terminal operations

The occurrence of natural disasters such as cyclones, earthquakes, and floods has increased worldwide, and their effect is most intense in localized regions. These events expose weaknesses in the power system infrastructures and show how well-prepared the system is to operate its services in a resilient way. During and after an extreme event, the outage and service disruption happens due to the inability of the affected part of the grid to cope with disruptions, which leads to insufficient resilience of the system. It is becoming more critical to enhance the resilience of electrical networks to extreme weather occurrences through suitable hardening processes and smart operational techniques. A reliable prevention approach necessitates a quantitative resilience metric that can estimate the effects of the future extreme events on distribution systems and assess the possible benefits of various planning strategies. This study aims to address the issue of lack of resilience metrics and proposes probabilistic metrics for evaluating the performance of distribution systems resilience in case of extreme weather events. Specifically, this study introduces active and passive resilience concepts to provide insights into system response. The proposed resilience metrics for various weather scenarios are quantified for the IEEE 33-bus system. The simulation framework also analyzes the effects of different operational and structural resilience improvement approaches on the proposed resilience metrics.

Cyber-physical systems are an integral component of weapons, sensors, and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.

Critical Infrastructures are infrastructures that provide vital or critical services for the population such as supply of electricity, drinking water, food, and health care. Interruptions of these services may have a direct or indirect adverse impact on the population's welfare. I.e. failures of critical services can lead to injuries or even losses of life, property damages, social and economic disruptions and thus drastically reduce the overall security of supply. We propose a new resilience measure that can be applied together with other known graph-related measures in order to achieve resilient, efficient, and economic feasible critical supply networks. In contrast to pure graph measures, the resilience measure we propose, takes spatial information of infrastructures and their criticalities w.r.t. to the availability of e.g. power or water into account. To enable a comprehensive evaluation of network topologies, a composite resilience metric is introduced, which uses these aforementioned measures as weighted building blocks. For evaluating critical network designs, the proposed methodology applies this composite resilience metric and Multi-Criteria Decision Analysis. Based on this, a genetic algorithm is proposed for identifying disaster resilient water supply networks.

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.

Resilience is regarded as an essential design objective of a wide range of systems in modern society. This work is based on a vision that networks of mobile energy storage systems could provide an alternative off-grid power system design for rural and underdeveloped regions. To evaluate the resiliency of networked energy storage systems under overload failure, a model of concurrent cascading failure and healing processes is developed and demonstrated. Two resilience metrics are used to evaluate the resilience of a real-world network, namely the recovery level at a specified time and the recovery time. The simulations generate system trajectories at each time step. We explore the dependence of the system behavior on different model parameters that capture key recovery strategies. The success probability of the recovery of a failed node needs to be high enough for the network to restore its original functionality. Similarly, the increase in recovery budget parameter also leads to faster and higher recovery levels. However, in most cases, there appears to be upper limits for both parameters, beyond which any further increase could not improve the recovery performance. There is an optimum portion of the loads of the active neighboring nodes that will be carried by the newly recovered node that results in the shortest recovery times or highest recovery levels. Our work sheds light on how to enhance networked systems resiliency by considering the optimization of various model parameters.

A data-driven approach for quantifying the resilience of railway networks

This paper presents the rationale behind performing an analysis of Internet resilience in the sense of maintaining a connection of autonomous systems in the presence of failures or attacks — on a level of a single country. Next, the graph of a network is constructed that represents interconnections between autonomous systems. The connectivity of the graph is examined for cases of link or node failure. Resilience metrics are proposed, focusing on a single autonomous system or on overall network reliability. The process of geographic location of networking infrastructure is presented, leading to an analysis of network resilience in the case of a joint failure of neighboring autonomous systems.

Water distribution networks (WDNs) play a crucial role in the well-being of human populations and economic prosperity. It is essential that they cope with abnormal operating conditions and recover functionality quickly. Traditionally, WDNs are designed using cost and reliability objectives, but there is a lack of consensus on the definition and quantification of reliability which typically is a computationally intense process. Subsequently, various reliability-like metrics, called resilience indices, have been developed and demonstrated in the design of WDNs. Few studies exist that thoroughly evaluate the performance of the previously developed resilience metrics. This paper investigates three resilience metrics by evaluating their performance on three benchmark WDNs for several simulated mechanical failure states. The metrics studied are: (a) resilience index, (b) network resilience index (NRI), and (c) modified resilience index (MRI). The metric MRI performed better overall but NRI produced cheaper designs that performed better in the case of WDN-I. It is recommended that a better metric that incorporates different dimensions of resilience, such as robustness and redundancy, should be developed in the future.

The COVID-19 global pandemic hit the aviation industry hard since the end of 2019. It has had an immediate, dramatic impact on airport traffic and revenue. Airports are the important nodes in the aviation system network, and the failure of a single airport can often affect the surrounding airports. The purpose of our analysis is to show how is the resilience and recovery of airports in the global public health crisis. Much research on resilience can be found in air transportation networks facing natural hazards or extreme weather, which focus on the robustness of the airport network. These methods are not suitable for the global public health event. Therefore, based on the collection of existing data, we combined with existing resilience measurement methods to analyze the resilience and recovery of airports during the global public health crisis. The resilience metrics results reflect the recovery of airports very well under different strategies. Here, we analyze airport network resilience by considering the performance-based methods. We integrate some metrics such as aircraft movements, passenger throughput, and freight throughput in the resilience metrics model, comparing the resilience evaluation under different preventive and control strategies, which can reflect the airport’s recovery speed during the COVID-19 pandemic. Our analysis indicates that the aviation system network deteriorates soon after the COVID-19 outbreak, but the recovery level of the aviation industry depends on what measures are taken to prevent and control the COVID-19 epidemic. In particular, the recovery of the aviation system network in Europe takes longer than in China, due to different prevention and control strategies for COVID-19. The study proves that the emergency response ability of the country for the public health crisis has a significant positive impact on speeding up the recovery of the aviation system.

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.

Power distribution systems face increasing threats from high-impact, low-probability (HILP) events caused by extreme weather conditions such as floods, typhoons, droughts, and heatwaves. These events often lead to power outages worldwide, highlighting the need for effective strategies to mitigate their impact. This work proposes an emergency response strategy that integrates network reconfiguration (NR) with the deployment of mobile emergency generators (MEGs) to enhance system resilience. The objective is to maximize power supply availability following HILP events. To achieve this, mixed-integer quadratic constraint programming (MIQCP) is used to optimize MEG deployment and network restoration. Additionally, improved quantitative resilience metrics are introduced to assess system degradation, pre-recovery, and recovery phases, enabling continuous resilience measurement and informed decision-making. Furthermore, an optimal capacity deployment strategy (OCDS) is proposed to ensure that MEGs are deployed with suitable capacities based on the specific needs of outage-affected areas. The effectiveness of the proposed strategy is demonstrated through tests on the IEEE 118 bus system. The results show a significant improvement of up to 100% in system resilience, reducing power outages and accelerating restoration. The findings confirm that integrating NR with optimized MEG deployment enhances service restoration, providing an effective approach for utilities to manage power disruptions.

This paper presents a novel approach to evaluating the resilience of urban electricity networks by integrating technical grid simulations with macroeconomic impact modelling. Focusing on seismic hazards in Auckland, New Zealand, the study applies OpenDSS and GridLab-D to simulate three distributed energy resource configurations: Business-as-Usual, HighRenewable Adoption, and Decentralized Microgrid. Resilience metrics including system average interruption duration, interruption frequency, outage duration, and energy not supplied are quantified and translated into economic outcomes using the MERIT (Measuring the Economics of Resilient Infrastructure through Time) model. Results indicate that decentralized configurations reduce energy not supplied by more than 80 percent and avoid up to $60 million New Zealand in gross regional product losses compared to the baseline. The findings demonstrate that decentralized microgrids not only improve technical performance but also deliver significant economic and social benefits. The integrated framework introduced in this study provides a scalable tool for infrastructure planners and policymakers to assess the value of resilience investments under potential future hazard scenarios.

Given the role of road tunnels in a road network, it is relevant to quantitatively assess their resilience due to disruptive events. In this respect, the travel speed of traffic flow, as a metric of resilience, in the event of traffic accidents in road tunnels has been scarcely used. This represents a gap of knowledge that this paper intends to fill. For the purpose, the research method applied involves the development of a traffic macro-simulation model, and the analysis of results using the average travel speed and its spatial profile as a resilience metric. Particularly, we have evaluated the resilience of a twin-tube motorway tunnel when a traffic accident occurs in a tube. The findings showed how the best functionality level of the system, expressed in terms of average vehicles’ speed, with its related greatest resilience index, is associated with the partial closure of the disrupted tube rather than the complete one. Further benefits might be obtained by activating Variable Message Signs (VMSs) that alert only the Heavy Goods Vehicles (HGVs) to exit the motorway before entering the tunnel and to use an alternative itinerary identified in the nearby transportation network. In this respect, we found that by means of the activation of VMSs as a traffic control strategy, the resilience index increased by about 7–17% when the traffic accident caused the partial closure of the disrupted tube for 1–3 h. Improvements might also be achieved by rapidly opening the two traffic by-passes at the tunnel portals that allow for the use of the adjacent undisrupted tube for two-way traffic. The unavailability of an alternative itinerary in the nearby transportation network along which to re-route the HGVs is also examined, finding a reduction in the functionality conditions of the system. The results obtained might serve as a support tool in the choice of functional recovery strategies in the case of the temporary partial or complete block of a tunnel tube due to a traffic accident.

The dependence on continuous availability of power for day‐to‐day functioning and sustenance of life is more critical today than ever before. A prolonged loss of power is no longer just an inconvenience, but brings normal life to a standstill. When Hurricane Sandy hit the U.S East Coast in October 2012, many lost their homes and properties due to the flooding and severe winds. However, a significant impact was the power outages to over 8 million customers across 21 states, for days and even weeks. This paper takes a systems view of these power outages from supply and demand sides, from a quantitative resilience perspective. The focus of this study is on the resilience action that enables restoration of the disrupted system. Generic resilience framework and metrics are used as the basis for the resilience analysis; the observations, lessons learnt, and recommendations based on this analysis are expected to improve recovery from large‐scale power outages resulting from any disruptive event in future.