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Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions

Author

Listed:
  • Shiwakoti, Nirajan
  • Sarvi, Majid
  • Rose, Geoff
  • Burd, Martin

Abstract

Collective movement is important during emergencies such as natural disasters or terrorist attacks, when rapid egress is essential for escape. The development of quantitative theories and models to explain and predict the collective dynamics of pedestrians has been hindered by the lack of complementary data under emergency conditions. Collective patterns are not restricted to humans, but have been observed in other non-human biological systems. In this study, a mathematical model for crowd panic is derived from collective animal dynamics. The development and validation of the model is supported by data from experiments with panicking Argentine ants (Linepithema humile). A first attempt is also made to scale the model parameters for collective pedestrian traffic from those for ant traffic, by employing a scaling concept approach commonly used in biology.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:45:y:2011:i:9:p:1433-1449
    DOI: 10.1016/j.trb.2011.05.016
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    References listed on IDEAS

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    1. Antonini, Gianluca & Bierlaire, Michel & Weber, Mats, 2006. "Discrete choice models of pedestrian walking behavior," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 667-687, September.
    2. Burd, Martin, 2006. "Ecological consequences of traffic organisation in ant societies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(1), pages 124-131.
    3. Nishinari, Katsuhiro & Sugawara, Ken & Kazama, Toshiya & Schadschneider, Andreas & Chowdhury, Debashish, 2006. "Modelling of self-driven particles: Foraging ants and pedestrians," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(1), pages 132-141.
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    Cited by:

    1. repec:eee:phsmap:v:482:y:2017:i:c:p:228-242 is not listed on IDEAS
    2. Thompson, Jason & Newnam, Sharon & Stevenson, Mark, 2015. "A model for exploring the relationship between payment structures, fatigue, crash risk, and regulatory response in a heavy-vehicle transport system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 204-215.
    3. Ma, Wanjing & Li, Li & Wang, Yinhai, 2016. "A driving force model for non-strict priority crossing behaviors of right-turn driversAuthor-Name: Lin, Dianchao," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 230-244.
    4. Saberi, Meead & Aghabayk, Kayvan & Sobhani, Amir, 2015. "Spatial fluctuations of pedestrian velocities in bidirectional streams: Exploring the effects of self-organization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 120-128.
    5. Chraibi, Mohcine & Ensslen, Tim & Gottschalk, Hanno & Saadi, Mohamed & Seyfried, Armin, 2016. "Assessment of models for pedestrian dynamics with functional principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 475-489.
    6. Haghani, Milad & Sarvi, Majid, 2017. "Social dynamics in emergency evacuations: Disentangling crowd’s attraction and repulsion effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 24-34.
    7. repec:eee:transb:v:99:y:2017:i:c:p:30-43 is not listed on IDEAS
    8. Li, Wenhang & Gong, Jianhua & Yu, Ping & Shen, Shen & Li, Rong & Duan, Qishen, 2014. "Simulation and analysis of individual trampling risk during escalator transfers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 119-133.
    9. repec:eee:transb:v:107:y:2018:i:c:p:253-294 is not listed on IDEAS
    10. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.
    11. Moonsoo Ko & Taewan Kim & Keemin Sohn, 2013. "Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation," Transportation, Springer, vol. 40(1), pages 91-107, January.
    12. 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.

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