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Empirical study of crowd dynamic in public gathering places during a terrorist attack event

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  • Wang, Jia
  • Ni, Shunjiang
  • Shen, Shifei
  • Li, Shuying

Abstract

Pedestrian dynamics in emergent situations such as stampedes and fire hazards have been widely studied, while few studies are focusing on human behavior in terrorist attack events. The research is vitally important for guiding efficient evacuation of the attacked crowd and reducing casualties. However, the research is insufficient due to the lack of researching materials. Besides, it is unconvincing for researchers to carry out these types of experiments. In this paper, the recorded video of the 3⋅1 Kunming terrorist attack event in China is analyzed. The instantaneous velocity field of the attacked crowd in the ticket hall is extracted from the video material, which is based on an image processing method. By analyzing the statistical characteristics of crowd velocity, crowd movement frequency and crowd entropy, several phenomena are found including crowd oscillating phenomenon, self-organizing grouping phenomenon. These discoveries will benefit the understanding of crowd dynamic in close-range attacks which is essential for the evacuation guidance efficiency, better design of architecture layout, and better defending strategies against the terrorists.

Suggested Citation

  • Wang, Jia & Ni, Shunjiang & Shen, Shifei & Li, Shuying, 2019. "Empirical study of crowd dynamic in public gathering places during a terrorist attack event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1-9.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:1-9
    DOI: 10.1016/j.physa.2019.01.120
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    Cited by:

    1. Liu, Jiaming & Zhang, Hui & Ding, Ning & Li, Yuntao, 2024. "A modified social force model for sudden attack evacuation based on Yerkes–Dodson law and the tendency toward low risk areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    2. 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).
    3. Guo, Chenglin & Huo, Feizhou & Li, Yufei & Li, Chao & Zhang, Jun, 2024. "An evacuation model considering pedestrian crowding and stampede under terrorist attacks," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    4. 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).
    5. Yu, Rongfu & Mao, Qinghua & Lv, Jian, 2022. "An extended model for crowd evacuation considering rescue behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    6. Yi, Ruolong & Du, Mingyu & Song, Weiguo & Zhang, Jun, 2024. "Fast trajectory extraction and pedestrian dynamics analysis using deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    7. Enrico Quagliarini & Fabio Fatiguso & Michele Lucesoli & Gabriele Bernardini & Elena Cantatore, 2021. "Risk Reduction Strategies against Terrorist Acts in Urban Built Environments: Towards Sustainable and Human-Centred Challenges," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    8. Jianlin, Li & Jun, Zhang & Xuehua, Song & Hang, Yu & Xintong, Li & Saizhe, Ding & Weiguo, Song, 2024. "The validation of pedestrian trajectories during turning and obstacle avoidance in virtual environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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