Quantifying the Resilience of an Urban Traffic-Electric Power Coupled System

Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective

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

A number of recent disasters have challenged the functionality of transport networks. The significance of road transport infrastructure to the functioning means that systems need to be able to operate under undesirable conditions, and quickly return to acceptable levels of service. The objective of the study is to analyze real-world networks speed fluctuation and evaluate the quantitative relationship between resilience and graph-based metrics, and link attributes using crowd-sourced data. We measure resilience in terms of the rate (vehicle speed) at which the road network recovers from a disruptive event and define five metrics to quantify network resilience. We analyze more than 500 links affected by disruptions in multiple cities with more than millions of crowd-sourced data. Using changes in link speed before, during, and after the disruption, the resilience metrics are applied to three case studies that are categorized as no-notice disruption, notice disruption, and disruption caused by continuous events. The results indicate that link graph-based metrics and attributes have a high impact on network resilience. However, the relevance of different metrics and attributes to the link resilience is different. Population density, predictability of disasters, and human factors have a significant impact on the reduction and recovery phases

Disaster management in smart cities

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.info:eu-repo/semantics/publishedVersio

Smart city and resilient city: Differences and connections

Smart city (SC) and resilient city (RC) have been studied and practiced over the years in terms of the increasing urban problems and disasters. However, there is a large overlap between their meanings and relationships. With an increasing concern for both SC and RC in urban development and hazard mitigation, a review was conducted to explore the differences and connections between SC and RC with scientometric analysis. There are far more literatures about SC than RC, and very few papers discuss SC and RC together. The research trend, category, and hotspots from research clusters are illustrated and compared. Major differences are discussed from their objectives, driving force, current research focus, and criticism. The literatures both related to SC and RC are used to explore their connections, which are very limited. The results revealed that the RC's impact on SC are positive from physical, social, and environmental aspects, while SC's impacts on RC could be both positive and negative from the above three aspects. It is indicated that SC and RC are both important for urban planning and can be complementary to each other through proper design and governance, which implies the need for building a resilient smart city (RSC). This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Visualization