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Reliable evacuation planning via demand inflation and supply deflation

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  • Ng, ManWo
  • Waller, S. Travis
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    Abstract

    This paper presents an evacuation route planning model that both accounts for demand uncertainty (i.e. the number of evacuees) as well as capacity uncertainty (i.e. the road capacities). To ensure reliability, the model plans for more evacuees (i.e. demand inflation) and less road capacity (i.e. supply deflation). A major contribution is that we provide a framework to determine the amount of demand inflation/supply deflation necessary to ensure a user-specified reliability level. The model is shown to be a natural generalization of previously proposed evacuation models. A small numerical case study reveals the key characteristics of the model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.

    Volume (Year): 46 (2010)
    Issue (Month): 6 (November)
    Pages: 1086-1094

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    Handle: RePEc:eee:transe:v:46:y:2010:i:6:p:1086-1094

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    Related research

    Keywords: Evacuation Uncertainty Dynamic traffic assignment Reliability Demand inflation Supply deflation;

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    Cited by:
    1. Ng, ManWo & Khattak, Asad & Talley, Wayne K., 2013. "Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 44-57.
    2. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
    3. Pruttipong Apivatanagul & Rachel Davidson & Linda Nozick, 2012. "Bi-level optimization for risk-based regional hurricane evacuation planning," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(2), pages 567-588, January.
    4. Ng, ManWo, 2013. "Partial link flow observability in the presence of initial sensors: Solution without path enumeration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 62-66.
    5. Bretschneider, S. & Kimms, A., 2012. "Pattern-based evacuation planning for urban areas," European Journal of Operational Research, Elsevier, vol. 216(1), pages 57-69.
    6. Duanmu, Jun & Chowdhury, Mashrur & Taaffe, Kevin & Jordan, Craig, 2012. "Buffering in evacuation management for optimal traffic demand distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 684-700.
    7. Ng, ManWo & Behr, Joshua & Diaz, Rafael, 2014. "Unraveling the evacuation behavior of the medically fragile population: Findings from hurricane Irene," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 122-134.

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