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Pedestrian emergency evacuation model based on risk field under attack event

Author

Listed:
  • Yu, Hang
  • Li, Xintong
  • Song, Weiguo
  • Zhang, Jun
  • Li, Xudong
  • Xu, Han
  • Jiang, Kechun

Abstract

In recent years, stabbings have occurred frequently around the world, often causing serious casualties and property losses. Therefore, using evacuation model to simulate and analyze this type of attack mode is of great significance to personnel safety and social stability. However, in the simulation process, the lack of interaction rules between pedestrians and attackers will seriously reduce the accuracy and credibility of the simulation result. Herein, this article focuses on the pedestrian evacuation under stabbings, we proposed a risk field model for pedestrian movement, its formula referred to the continuous point-source diffusion equation in unbounded space. The influence of the surrounding environment on pedestrian movement was considered comprehensively to set the direction of the force exerted by the attacker, pedestrian movement rules and exit selection mechanism were introduced in details. At the same time, we discuss two different attack strategies for attackers, and carried out virtual reality experiments to analyze the experimenter’s attack modes and compare them with the two pure strategies. Multiple simulations are conducted on the basis of the model to explore the physical significance of model parameters and the influence of attack mode and location on evacuation, and to explore the mechanism of pedestrians’ selection of exits under the condition of multiple exits. At the end of this paper, the simulation results are summarized and suggestions for evacuation under emergencies are provided, which may be helpful to the simulation of pedestrian evacuation under emergencies in the future.

Suggested Citation

  • Yu, Hang & Li, Xintong & Song, Weiguo & Zhang, Jun & Li, Xudong & Xu, Han & Jiang, Kechun, 2022. "Pedestrian emergency evacuation model based on risk field under attack event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
  • Handle: RePEc:eee:phsmap:v:606:y:2022:i:c:s0378437122006896
    DOI: 10.1016/j.physa.2022.128111
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    References listed on IDEAS

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    Cited by:

    1. Wang, Kun & Xiong, Li & Xue, Rudan, 2024. "Real-time data stream learning for emergency decision-making under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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