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Driving-forces model on individual behavior in scenarios considering moving threat agents

Author

Listed:
  • Li, Shuying
  • Zhuang, Jun
  • Shen, Shifei
  • Wang, Jia

Abstract

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places.

Suggested Citation

  • Li, Shuying & Zhuang, Jun & Shen, Shifei & Wang, Jia, 2017. "Driving-forces model on individual behavior in scenarios considering moving threat agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 127-140.
    • Handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:127-140
    DOI: 10.1016/j.physa.2017.04.011
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    References listed on IDEAS

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    Cited by:

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    2. Can Liao & Kejun Zhu & Haixiang Guo & Jian Tang, 2019. "Simulation Research on Safe Flow Rate of Bidirectional Crowds Using Bayesian-Nash Equilibrium," Complexity, Hindawi, vol. 2019, pages 1-15, January.
    3. Li, Lihua & Ding, Ning & Ma, Yaping & Zhang, Hui & Jin, Hua, 2020. "Social Relation Network and group behavior based on evacuation experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(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. 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.

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