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An aggregate approach to model evacuee behavior for no-notice evacuation operations

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  • Yu-Ting Hsu
  • Srinivas Peeta

Abstract

This study proposes an aggregate approach to model evacuee behavior in the context of no-notice evacuation operations. It develops aggregate behavior models for evacuation decision and evacuation route choice to support information-based control for the real-time stage-based routing of individuals in the affected areas. The models employ the mixed logit structure to account for the heterogeneity across the evacuees. In addition, due to the subjectivity involved in the perception and interpretation of the ambient situation and the information received, relevant fuzzy logic variables are incorporated within the mixed logit structure to capture these characteristics. Evacuation can entail emergent behavioral processes as the problem is characterized by a potential threat from the extreme event, time pressure, and herding mentality. Simulation experiments are conducted for a hypothetical terror attack to analyze the models’ ability to capture the evacuation-related behavior at an aggregate level. The results illustrate the value of using a mixed logit structure when heterogeneity is pronounced. They further highlight the benefits of incorporating fuzzy logic to enhance the prediction accuracy in the presence of subjective and linguistic elements in the problem. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Yu-Ting Hsu & Srinivas Peeta, 2013. "An aggregate approach to model evacuee behavior for no-notice evacuation operations," Transportation, Springer, vol. 40(3), pages 671-696, May.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:3:p:671-696
    DOI: 10.1007/s11116-012-9440-7
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    References listed on IDEAS

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    Cited by:

    1. Herrera-Restrepo, Oscar & Triantis, Konstantinos & Trainor, Joseph & Murray-Tuite, Pamela & Edara, Praveen, 2016. "A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach," Omega, Elsevier, vol. 60(C), pages 45-59.
    2. Junji Urata & Adam J. Pel, 2018. "People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 889-905, May.
    3. Ma. Lim & Hector Lim & Mongkut Piantanakulchai & Francis Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.
    4. Shahparvari, Shahrooz & Chhetri, Prem & Abbasi, Babak & Abareshi, Ahmad, 2016. "Enhancing emergency evacuation response of late evacuees: Revisiting the case of Australian Black Saturday bushfire," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 148-176.
    5. Zhi-Chun Li & Qian Liu, 2020. "Optimal deployment of emergency rescue stations in an urban transportation corridor," Transportation, Springer, vol. 47(1), pages 445-473, February.
    6. Ma. Bernadeth B. Lim & Hector R. Lim & Mongkut Piantanakulchai & Francis Aldrine Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.
    7. Xiaozheng He & Srinivas Peeta, 2014. "Dynamic Resource Allocation Problem for Transportation Network Evacuation," Networks and Spatial Economics, Springer, vol. 14(3), pages 505-530, December.
    8. Yu-Ting Hsu & Srinivas Peeta, 2015. "Clearance Time Estimation for Incorporating Evacuation Risk in Routing Strategies for Evacuation Operations," Networks and Spatial Economics, Springer, vol. 15(3), pages 743-764, September.

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