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Evacuation Modelling in the United States: Does the Demand Model Choice Matter?

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  • Mustafa Anil Yazici
  • Kaan Ozbay

Abstract

This paper reviews the literature on the evacuation demand problem, with an emphasis on the impact of various modelling approaches on network‐wide evacuation performance measures. First, a number of important factors that affect evacuee behaviour are summarized. Evacuation software packages and tools are also investigated in terms of the demand generation model they use. The most widely used models are then selected for performing sensitivity analysis. Next, a cell‐transmission‐based system optimal dynamic traffic assignment (SO‐DTA) model is employed to assess the effects of the demand model choice on the clearance time and average travel time. It is concluded that evacuation demand models should be selected with care, and policy makers should make sure the selected demand curve can replicate real‐life conditions with relatively high fidelity for the study region to be able to develop reliable and realistic evacuation plans.

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  • Mustafa Anil Yazici & Kaan Ozbay, 2008. "Evacuation Modelling in the United States: Does the Demand Model Choice Matter?," Transport Reviews, Taylor & Francis Journals, vol. 28(6), pages 757-779, March.
    • Handle: RePEc:taf:transr:v:28:y:2008:i:6:p:757-779
    DOI: 10.1080/01441640802041812
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    Cited by:

    1. Piyapong Suwanno & Chaiwat Yaibok & Noriyasu Tsumita & Atsushi Fukuda & Kestsirin Theerathitichaipa & Manlika Seefong & Sajjakaj Jomnonkwao & Rattanaporn Kasemsri, 2023. "Estimation of the Evacuation Time According to Different Flood Depths," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    2. Yulei Song & Xuedong Yan, 2016. "A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model," IJERPH, MDPI, vol. 13(10), pages 1-21, October.
    3. Madireddy, Manini & Kumara, Soundar & Medeiros, D.J. & Shankar, Venky N., 2015. "Leveraging social networks for efficient hurricane evacuation," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 199-212.
    4. Yi, Wenqi & Nozick, Linda & Davidson, Rachel & Blanton, Brian & Colle, Brian, 2017. "Optimization of the issuance of evacuation orders under evolving hurricane conditions," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 285-304.
    5. Douglas Bish & Edward Chamberlayne & Hesham Rakha, 2013. "Optimizing Network Flows with Congestion-Based Flow Reductions," Networks and Spatial Economics, Springer, vol. 13(3), pages 283-306, September.
    6. Ayşe Polat & Hüseyin Onur Tezcan, 2024. "Investigation of Trip Decisions for an Earthquake: A Case Study in Elazığ, Türkiye," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    7. Xu, Kecheng & Davidson, Rachel A. & Nozick, Linda K. & Wachtendorf, Tricia & DeYoung, Sarah E., 2016. "Hurricane evacuation demand models with a focus on use for prediction in future events," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 90-101.
    8. Ding Wang & Kaan Ozbay & Zilin Bian, 2021. "Modeling and Analysis of Optimal Strategies for Leveraging Ride-Sourcing Services in Hurricane Evacuation," Sustainability, MDPI, vol. 13(8), pages 1-22, April.

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