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A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition

Author

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  • Weifeng, Yuan
  • Kang Hai, Tan

Abstract

A cellular automata (CA) model, which adopts the findings of tenability analysis, is proposed to simulate the evacuation from a smoke-filled room. Two algorithms, viz., direct algorithm and indirect algorithm, are used to model the behavior of a crowd consisting of people with different movement velocities. In the indirect algorithm, the movement velocity is related to probability so that the CPU time is greatly reduced. Another novelty is that an experimental formula for estimating the survival duration when exposed to constant concentration of toxic gases in a static environment is extended to one that involves varying degree of toxic gases. This has been incorporated into the CA model.

Suggested Citation

  • Weifeng, Yuan & Kang Hai, Tan, 2007. "A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 250-262.
    • Handle: RePEc:eee:phsmap:v:379:y:2007:i:1:p:250-262
    DOI: 10.1016/j.physa.2006.12.044
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    Citations

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    Cited by:

    1. Zhou, Zi-Xuan & Nakanishi, Wataru & Asakura, Yasuo, 2021. "Route choice in the pedestrian evacuation: Microscopic formulation based on visual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    2. Fu, Zhijian & Zhou, Xiaodong & Zhu, Kongjin & Chen, Yanqiu & Zhuang, Yifan & Hu, Yuqi & Yang, Lizhong & Chen, Changkun & Li, Jian, 2015. "A floor field cellular automaton for crowd evacuation considering different walking abilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 294-303.
    3. Cao, Shuchao & Song, Weiguo & Lv, Wei & Fang, Zhiming, 2015. "A multi-grid model for pedestrian evacuation in a room without visibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 45-61.
    4. 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.
    5. Wang, Li & Liu, Mao & Meng, Bo, 2013. "Incorporating topography in a cellular automata model to simulate residents evacuation in a mountain area in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 520-528.
    6. Fu, Zhijian & Luo, Lin & Yang, Yue & Zhuang, Yifan & Zhang, Peitong & Yang, Lizhong & Yang, Hongtai & Ma, Jian & Zhu, Kongjin & Li, Yanlai, 2016. "Effect of speed matching on fundamental diagram of pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 31-42.
    7. Luo, Lin & Liu, Xiaobo & Fu, Zhijian & Ma, Jian & Liu, Fanxiao, 2020. "Modeling following behavior and right-side-preference in multidirectional pedestrian flows by modified FFCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    8. Zhang, Dezhen & Huang, Gaoyue & Ji, Chengtao & Liu, Huiying & Tang, Ying, 2021. "Pedestrian evacuation modeling and simulation in multi-exit scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    9. Zhou, Xuemei & Hu, Jingjie & Ji, Xiangfeng & Xiao, Xiongziyan, 2019. "Cellular automaton simulation of pedestrian flow considering vision and multi-velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 982-992.
    10. Zhang, Xinwei & Zhang, Peihong & Zhong, Maohua, 2021. "A dual adaptive cellular automaton model based on a composite field and pedestrian heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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