Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Fanny Kristiansson: Senior Hannah Russel: Graduate Student
Lead Presenter's Name
Fanny Kristiansson
Faculty Mentor Name
Scott Parr
Loading...
Abstract
The National Science Foundation’s definition of resiliency is “the ability to prepare and plan for, absorb, recover from, or more successfully adapt to actual or potential adverse events” (National Science Foundation, 2016). While this definition is informative and useful, it lacks a quantitative reference. There is a need for a method of quantifying resilience to better plan and prepare for system wide disruptions. The research effort described herein provides a quantifiable measures of system resiliency, consistent with NSF’s definition. Fundamentally, a system disruption can be partitioned into five distinctive states: the stable pre-event state, the absorption state, the disrupted state, the recovered state, and stable recovered state. The proposed method identifies these states by measuring system output and quantifies each component on a value scale between zero and one. The resiliency measure then unifies these metrics to provide an overall assessment of resiliency, which accounts for the system’s ability to absorb, recover, and adapt. This approach to quantifying resiliency is applicable to any real-world or simulated system with measurable outputs. This paper first documents the development of the resiliency quantification method and then applies the method toward five complex, real world, transportation systems undergoing disruptions. These case studies consisted of six maritime port, three airports, three power grids, two localized refueling systems, and the Colorado Department of Transportation’s cyber network. Each system had a measurable drop in functionality due to a disruption. In general the results of this research showed that the proposed method of quantifying resiliency can be utilized for any transportation system.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Student Internal Grants
Methodology For Quantifying Resiliency of Transportation Systems
The National Science Foundation’s definition of resiliency is “the ability to prepare and plan for, absorb, recover from, or more successfully adapt to actual or potential adverse events” (National Science Foundation, 2016). While this definition is informative and useful, it lacks a quantitative reference. There is a need for a method of quantifying resilience to better plan and prepare for system wide disruptions. The research effort described herein provides a quantifiable measures of system resiliency, consistent with NSF’s definition. Fundamentally, a system disruption can be partitioned into five distinctive states: the stable pre-event state, the absorption state, the disrupted state, the recovered state, and stable recovered state. The proposed method identifies these states by measuring system output and quantifies each component on a value scale between zero and one. The resiliency measure then unifies these metrics to provide an overall assessment of resiliency, which accounts for the system’s ability to absorb, recover, and adapt. This approach to quantifying resiliency is applicable to any real-world or simulated system with measurable outputs. This paper first documents the development of the resiliency quantification method and then applies the method toward five complex, real world, transportation systems undergoing disruptions. These case studies consisted of six maritime port, three airports, three power grids, two localized refueling systems, and the Colorado Department of Transportation’s cyber network. Each system had a measurable drop in functionality due to a disruption. In general the results of this research showed that the proposed method of quantifying resiliency can be utilized for any transportation system.