Utility Mobile

CRISP Project graphic, storm clouds over the ocean

CRISP 2.0 Type 2: Collaborative Research: Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes (ORDER-CRISP)

Sponsor

National Science Foundation

We aim to conduct transformative research on physical and social aspects of coastal vulnerability embedded in interdependent infrastructures by developing a highly integrated modeling framework. We establish the notion of decentralized resilience as a cascade-proofing mechanism in Critical Interdependent-infrastructure Systems and Processes (CRISP) to expand the landscape of thinking and planning resilience. We propose an innovative model to investigate and evaluate the coupled nature of vulnerabilities across physical and social systems at both micro and macro scales. We offer a comparative approach based on two highly vulnerable, heterogeneous (e.g., in location and social structure) cities on the U.S. coasts (Miami and Houston). The novelty lies in integrating five interdisciplinary research components: (i) incorporating wind and flood inundation risk into the utility and service disruption models to analyze and determine the extent of interdependent infrastructure failures in energy, water, transportation, and telecommunication sectors, (ii) constructing socio-infrastructural systems of vulnerability and analyzing evacuation/relocation behavior to assess the need for emergent critical infrastructure services, (iii) microsimulations for analyzing coping behaviors and facilitating decentralized resilience through information sharing and critical resource pooling, (iv) a macro ( city-level) inoperability-based resilience model to integrate household and social responses with disrupted interdependent infrastructure systems, and (v) an app to facilitate participatory resilience (RCROWD) through crowd-sourcing. New concepts, data, methods, and findings will deliver a scalable methodology to enhance resilience in hazard-prone coastal cities and beyond.

Project Overview

The goals of the UVA components of the overall project are to model the hurricane evacuation decision-making process and interventions such as incentives to evacuate, understand behaviors (e.g., sharing resources and information) that will aid people who remain behind (i.e., do not evacuate) to better survive the hurricane and its aftermath [resiliency], and develop a web-based application (i.e., web app) to aid people in need of assistance during a hurricane event.

Findings

In the first four years of this project, we have published 35 papers on evacuation modeling and resilience, collective action modeling, group behavior, agent-based modeling and methods, contagion modeling, and software infrastructures. Among specific findings is that neglecting family concerns over looting in modeling families’ decisions to evacuate in the face of an oncoming hurricane can overestimate the number of families who will evacuate by up to 50% (Figure 1). The web app prototype for resource sharing has been completed and evaluated by a focus group (Figure 2).

     

Figure 1. Simulation results of modeling the evacuation behavior among families in VA Beach, VA. Left: results for the standard model. The fraction of the population deciding to evacuate (Frac. DE) is plotted as a function of time in days (day 10 is hurricane landfall) for different numbers (ns) of families that are seeded as evacuating (state 1) at a time (t=0). Right: analogous temporal simulation results for the 2-mode-threshold model. The fraction of families evacuating can decrease by 50%. The networks are Kleinberg small-world networks with a short-range distance of 40 m number q of long-range edges of 16, and r=2.5.

Team

Professor

Professor of Public Health Sciences, School of Medicine

Professor

Professor of Computer Science, School of Engineering and Applied Science

Research Scientist

Publications
Network Systems Science and Advanced Computing
Carscadden H; Kuhlman C; Marathe M; Ravi S; Rosenkrantz D . Network Science. Cambridge University Press. 2022; 10(3):234-260
Network Systems Science and Advanced Computing
Kuhlman C; Marathe A; Vullikanti A; Halim N; Mozumder P . Social Network Analysis and Mining volume. Springer. 2021; 12
Network Systems Science and Advanced Computing
Kuhlman C; Marathe A; Vullikanti A; Halim N; Mozumder P . Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. 2020; :654-662