Modeling Emergency Managers’ Hurricane Evacuation Decisions
| Autor: | Chester G. Wilmot, Ravindra Gudishala |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
050210 logistics & transportation
021110 strategic defence & security studies Engineering Decision support system Jurisdiction Operations research Notice Emergency management business.industry Mechanical Engineering 05 social sciences 0211 other engineering and technologies Storm 02 engineering and technology Newspaper 0502 economics and business Threatened species Operations management Hurricane evacuation business Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2604:82-87 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.3141/2604-10 |
| Popis: | Emergency management and decision support system (EMDSS) tools play an important role in assisting emergency managers with making important decisions about the movement of people to safety when a jurisdiction is threatened by a storm. One of the important components of an EMDSS is an evacuation demand model that predicts whether and when households will evacuate when they are threatened by a storm. A critical input to that model is an emergency manager's decision to issue an evacuation notice. No existing mathematical models predict whether and when an emergency manager will issue an evacuation notice on the basis of a hurricane forecast and other contextual factors. To fill this gap, this research study sought to develop a model that would predict if and when an emergency manager would issue an evacuation notice when a jurisdiction was threatened by a storm. Data from poststorm assessment surveys and newspaper archives were used to retrieve past decisions made by evacuation managers for five storms in 45 coastal counties or parishes. The data were then used to develop a discrete choice model by use of the time-dependent sequential logit paradigm. Five independent predictor variables—storm surge, clearance time, time to landfall, hurricane category, and time of day—were found to be good predictors of the decisions made by emergency managers. This model could be useful to emergency managers to estimate how other emergency managers decide to evacuate an area when they are faced with an evacuation decision. The model could also benefit researchers and practitioners engaged in modeling and understanding hurricane evacuation behavior. |
| Databáze: | OpenAIRE |
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