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Buffering in evacuation management for optimal traffic demand distribution

Author

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  • Duanmu, Jun
  • Chowdhury, Mashrur
  • Taaffe, Kevin
  • Jordan, Craig

Abstract

This paper presents a new framework for managing congestion during emergency evacuations. The algorithm allows a long link of the network to be used as a buffer to keep the traffic flow moving in. Concurrently, a detour trigger time is estimated to keep the traffic under-saturated in the buffer zone and minimize the total travel time. The integration algorithm presented in this paper is an efficient mathematical solution for travel time cost calculation. A case study is presented to demonstrate the efficacy of the traffic demand buffering strategy developed in this research for managing the evacuation flow.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transe:v:48:y:2012:i:3:p:684-700
    DOI: 10.1016/j.tre.2011.12.002
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    References listed on IDEAS

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    1. Lee D. Han & Fang Yuan & Shih-Miao Chin & Holing Hwang, 2006. "Global Optimization of Emergency Evacuation Assignments," Interfaces, INFORMS, vol. 36(6), pages 502-513, December.
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    Citations

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    Cited by:

    1. Jingyi Hua & Gang Ren & Yang Cheng & Chen Yu & Bin Ran, 2015. "Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(7), pages 777-794, October.
    2. Praveen Maghelal & Xiangyu Li & Walter Gillis Peacock, 2017. "Highway congestion during evacuation: examining the household’s choice of number of vehicles to evacuate," 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. 87(3), pages 1399-1411, July.
    3. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    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. Ng, ManWo & Diaz, Rafael & Behr, Joshua, 2015. "Departure time choice behavior for hurricane evacuation planning: The case of the understudied medically fragile population," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 215-226.
    6. Jiang-Hua Zhang & Hai-Yue Liu & Rui Zhu & Yang Liu, 2017. "Emergency Evacuation of Hazardous Chemical Accidents Based on Diffusion Simulation," Complexity, Hindawi, vol. 2017, pages 1-16, December.

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