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A Stairs Evacuation Model Considering the Pedestrian Merging Flows

Author

Listed:
  • Xia Zhong Zheng
  • Dan Tian
  • Ming Zhang
  • Chaoran Hu
  • Liyang Tong

Abstract

Pedestrian merging flows are common in a stairs evacuation process, which involves complex interactions among pedestrians that substantially restrict the efficiency of the stairs evacuation process. Analyzing the pedestrian merging flows process and improving the efficiency of stairs evacuation are urgent and essential tasks. A novel simplified stairs evacuation model for simulating and analyzing the stairs evacuation process, which considers the impact of merging flows, is proposed in this process. The dynamic pedestrian output rate of a floor platform is calculated by the number of pedestrians on the floor platform. The merging ratio determined by the design size of stairs is adopted to determine the ratio between the stairs pedestrian flow and the floor pedestrian flow in the pedestrian output rate of the floor platform. To evaluate the stairs evacuation process is divided into three stages based on the pedestrian merging flows process, and the evacuation time at each stage is computed by the dynamic pedestrian output rate of the floor platform. The stairs evacuation capacity is calculated by the evacuation time and the number of pedestrians. A case study of a six-floor building evacuation is investigated, and the reliability and feasibility of the proposed model is verified. By establishing different merging ratios, the optimal merging ratio is obtained by comparing the evacuation capacities of different merging ratios, which provides a reference of stairs design for designers.

Suggested Citation

  • Xia Zhong Zheng & Dan Tian & Ming Zhang & Chaoran Hu & Liyang Tong, 2019. "A Stairs Evacuation Model Considering the Pedestrian Merging Flows," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-11, December.
    • Handle: RePEc:hin:jnddns:7615479
    DOI: 10.1155/2019/7615479
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    References listed on IDEAS

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    1. Ezaki, Takahiro & Yanagisawa, Daichi & Ohtsuka, Kazumichi & Nishinari, Katsuhiro, 2012. "Simulation of space acquisition process of pedestrians using Proxemic Floor Field Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 291-299.
    2. Lovreglio, Ruggiero & Ronchi, Enrico & Nilsson, Daniel, 2015. "Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 308-320.
    3. Pursals, Salvador Casadesús & Garzón, Federico Garriga, 2009. "Optimal building evacuation time considering evacuation routes," European Journal of Operational Research, Elsevier, vol. 192(2), pages 692-699, January.
    4. Ning Ding & Hui Zhang & Tao Chen, 2017. "Simulation-based optimization of emergency evacuation strategy in ultra-high-rise buildings," 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. 89(3), pages 1167-1184, December.
    5. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    6. Guo, Fang & Li, Xingli & Kuang, Hua & Bai, Yang & Zhou, Huaguo, 2016. "An extended cost potential field cellular automata model considering behavior variation of pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 630-640.
    7. Younggi Park & Youngman Lee & Junyoung Na & Hong Sun Ryou, 2019. "Numerical Study on the Effect of Tunnel Aspect Ratio on Evacuation with Unsteady Heat Release Rate Due to Fire in the Case of Two Vehicles," Energies, MDPI, vol. 12(1), pages 1-12, January.
    8. Liu, Qian, 2018. "A social force model for the crowd evacuation in a terrorist attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 315-330.
    9. Zeng, Yiping & Song, Weiguo & Jin, Sha & Ye, Rui & Liu, Xiaodong, 2017. "Experimental study on walking preference during high-rise stair evacuation under different ground illuminations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 26-37.
    10. Lei, Wenjun & Li, Angui & Gao, Ran & Wang, Xiaowei, 2012. "Influences of exit and stair conditions on human evacuation in a dormitory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6279-6286.
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