TRANSPORTATION RESILIENCE ARCHITECTURE: A FREMEWORK FOR ANALYSIS OF INFRASTRUCTURE, AGENCY AND USERS

Abstract

How do some countries, or sectors of it, overcome potentially disastrous events while others fail at it? The answer lies on the concept of resilience, and its importance grows as our environment’s deterioration escalates, limiting the access to economic, social, and natural resources. This study evaluates resilience from a transportation perspective and defines it as “the ability for the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (Heaslip, Louisell, & Collura, 2009). The literature shows that previous evaluation approaches usually do not directly integrate all perspectives of a transportation system. In this manner, this study introduces the concept of Transportation Resilience Architecture (TRA) as a framework for evaluating resilience of a transportation system through the cumulative effect of a system’s Infrastructure, Agency and User layer.

This research introduces three quantitative methodologies as a way to evaluate resilience through TRA. For Infrastructure, a practical tool for measuring the level of accessibility to “safe zones” is presented, which takes advantage of the logsum measure resulting from Statewide Transportation Models. Results from the two locations analyzed (Frederick, MD and Anacostia, D.C.) suggest a positive correlation between income and accessibility. For Agency, metrics collected through a thorough literature review where combined with survey data to develop an evaluation framework based on Fuzzy Algorithms that yields to an index. The end product highlights the importance of interoperability as a disaster preparedness and response enhancing practice. Finally, for User, a dynamic discrete choice model was adapted to evaluate evacuation behavior, taking into account the disaster’s characteristics and the population’s expectations of them—a first from an evacuation perspective. The proposed framework is estimated using SP evacuation data collected on Louisiana residents. The result indicates that the dynamic discrete choice model excels in incorporating demographic information of respondents, a key input in policy evaluation, and yields significantly more accurate evacuation percentages per forecast.