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Understanding pedestrian crowd panic: a review on model organisms approach

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  • Shiwakoti, Nirajan
  • Sarvi, Majid

Abstract

Understanding how crowds behave during collective displacement is at the heart of both pedestrian traffic engineering and ‘movement ecology’. Perhaps the most critical reason for studying collective human dynamics under emergency/panic conditions is the lack of complementary data to develop and validate an explanatory model. A little used alternative is to study non-human model organisms. In this paper, we review experiences in using non-human organisms to study crowd panic in the literature. We then highlight the potential contribution that research with biological entities could make to understand the complex pedestrian behaviour and the enhancement of pedestrian safety during emergency/panic conditions. We also emphasise that understanding of behavioural similarities and dissimilarities between humans and animals is required for developing a good experimental design aimed to study collective behaviour. A generic model that could describe the common underlying mechanisms of crowd behaviour among organisms of different body sizes is identified as future challenge.

Suggested Citation

  • Shiwakoti, Nirajan & Sarvi, Majid, 2013. "Understanding pedestrian crowd panic: a review on model organisms approach," Journal of Transport Geography, Elsevier, vol. 26(C), pages 12-17.
  • Handle: RePEc:eee:jotrge:v:26:y:2013:i:c:p:12-17
    DOI: 10.1016/j.jtrangeo.2012.08.002
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    References listed on IDEAS

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    1. Dirk Helbing & Illés Farkas & Tamás Vicsek, 2000. "Simulating dynamical features of escape panic," Nature, Nature, vol. 407(6803), pages 487-490, September.
    2. Dirk Helbing & Lubos Buzna & Anders Johansson & Torsten Werner, 2005. "Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions," Transportation Science, INFORMS, vol. 39(1), pages 1-24, February.
    3. Shiwakoti, Nirajan & Sarvi, Majid & Rose, Geoff & Burd, Martin, 2011. "Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1433-1449.
    4. Audrey Dussutour & Vincent Fourcassié & Dirk Helbing & Jean-Louis Deneubourg, 2004. "Optimal traffic organization in ants under crowded conditions," Nature, Nature, vol. 428(6978), pages 70-73, March.
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    Citations

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    Cited by:

    1. Chen, Changkun & Sun, Huakai & Lei, Peng & Zhao, Dongyue & Shi, Congling, 2021. "An extended model for crowd evacuation considering pedestrian panic in artificial attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    2. Lin, Peng & Ma, Jian & Liu, Tianyang & Ran, Tong & Si, Youliang & Li, Tao, 2016. "An experimental study of the “faster-is-slower” effect using mice under panic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 157-166.
    3. Shi, Xiaomeng & Ye, Zhirui & Shiwakoti, Nirajan & Tang, Dounan & Lin, Junkai, 2019. "Examining effect of architectural adjustment on pedestrian crowd flow at bottleneck," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 350-364.
    4. Haghani, Milad & Sarvi, Majid, 2018. "Crowd behaviour and motion: Empirical methods," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 253-294.
    5. Shabna SayedMohammed & Anshi Verma & Charitha Dias & Wael Alhajyaseen & Abdulkarim Almukdad & Kayvan Aghabayk, 2022. "Crowd Evacuation through Crossing Configurations: Effect of Crossing Angles and Walking Speeds on Speed Variation and Evacuation Time," Sustainability, MDPI, vol. 14(22), pages 1-21, November.

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