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Public Evacuation Decisions and Hurricane Track Uncertainty

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  • Eva Regnier

    ()
    (Defense Resources Management Institute, Naval Postgraduate School, Monterey, California 93943)

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    Abstract

    Public officials with the authority to order hurricane evacuations face a difficult trade-off between risks to life and costly false alarms. Evacuation decisions must be made on the basis of imperfect information, in the form of forecasts. The quality of these decisions can be improved if they are also informed by measures of uncertainty about the forecast, including estimates of the value of waiting for updated, more accurate, forecasts. Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions. Typical evacuation clearance times and track uncertainty imply that public officials who require no more than a 10% probability of failing to evacuate before a striking hurricane (a false negative) must accept that at least 76%--and for some locations over 90%--of evacuations will be false alarms. Reducing decision lead times from 72 to 48 hours for major population centers could save an average of hundreds of millions of dollars in evacuation costs annually, with substantial geographic variation in savings.

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    File URL: http://dx.doi.org/10.1287/mnsc.1070.0764
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 54 (2008)
    Issue (Month): 1 (January)
    Pages: 16-28

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    Handle: RePEc:inm:ormnsc:v:54:y:2008:i:1:p:16-28

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    Related research

    Keywords: decision analysis; risk; natural systems; disaster planning; public evacuations;

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    Cited by:
    1. Zhujun Liu & Jiuping Xu & Bernard Han, 2013. "Small- and medium-sized enterprise post-disaster reconstruction management patterns and application," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 809-835, September.
    2. Samohyl, Robert, 2012. "Audits and logistic regression, deciding what really matters in service processes: a case study of a government funding agency for research grants," MPRA Paper 41557, University Library of Munich, Germany.
    3. J. Senkbeil & D. Brommer & P. Dixon & M. Brown & K. Sherman-Morris, 2010. "The perceived landfall location of evacuees from Hurricane Gustav," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(1), pages 141-158, July.
    4. Corene Matyas & Sivaramakrishnan Srinivasan & Ignatius Cahyanto & Brijesh Thapa & Lori Pennington-Gray & Jorge Villegas, 2011. "Risk perception and evacuation decisions of Florida tourists under hurricane threats: a stated preference analysis," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(2), pages 871-890, November.
    5. Bretschneider, S. & Kimms, A., 2011. "A basic mathematical model for evacuation problems in urban areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 523-539, July.
    6. McLay, Laura A. & Boone, Edward L. & Brooks, J. Paul, 2012. "Analyzing the volume and nature of emergency medical calls during severe weather events using regression methodologies," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 55-66.
    7. Bish, Douglas R. & Sherali, Hanif D., 2013. "Aggregate-level demand management in evacuation planning," European Journal of Operational Research, Elsevier, vol. 224(1), pages 79-92.

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