An Integrated Scenario Ensemble‐Based Framework for Hurricane Evacuation Modeling: Part 1—Decision Support System. Issue 1 (30th March 2018)

• Record Type: Journal Article
• Authors: Davidson, Rachel A.; Nozick, Linda K.; Wachtendorf, Tricia; Blanton, Brian; Colle, Brian; Kolar, Randall L.; DeYoung, Sarah; Dresback, Kendra M.; Yi, Wenqi; Yang, Kun; Leonardo, Nicholas
• Is Part Of: Risk analysis. Volume 40:Issue 1(2020)
• Page Start: 97
• Page End: 116
• Publication Date: 2018-03-30
• Subjects: Evacuation -- hurricane -- multistage stochastic program; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12990
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: This article introduces a new integrated scenario‐based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human–natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well‐hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two‐stage stochastic program.