Agent-based modeling and simulations of terrorist attacks combined with stampedes

被引:26
|
作者
Lu, Peng [1 ]
Zhang, Zhuo [1 ]
Li, Mengdi [1 ]
Chen, Dianhan [1 ]
Yang, Hou [1 ]
机构
[1] Cent South Univ, Dept Sociol, Changsha, Peoples R China
基金
中国国家自然科学基金;
关键词
Attack mechanism; Agent-based modeling; Simulations; Stampede mechanism; Perception range; Collision damage; EVACUATION; CROWD; SOCIOPHYSICS; ECONOPHYSICS; SELECTION; DECISION; BEHAVIOR; SYSTEM; IMPACT;
D O I
10.1016/j.knosys.2020.106291
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As a global problem, the terrorism leads to high death tolls each year. During terrorist attacks, the direct death is caused by terrorists attacking civilians. However, indirect death caused by stampedes should not be underestimated. Under great panic, most civilians were running disorderly, rushing into limited number of Exits, which causes stampede injuries and deaths. To explore this dual-mechanism dynamics, we build the agent-based modeling of particle system. Civilians lose blood when attacked, which is the attack mechanism, or crashed and trampled by others civilians, which is the stampede mechanism. For all civilians, the blood variable determines the physical status of being strong, healthy, weak, injured, and dead. Five key factors, such as the perception range, the number of Exits, the density of civilians, the number of terrorists, and attack strategies, are introduced into the model. We run each simulation repeatedly for multiple times and take the averaged survival rate, attack death, and stampede death as robust outcomes. The collision damage has the phase transition effects between stampede and attack deaths. The perception range R have the peak effect on the survival rate and the trough effect on both stampede and attack deaths. It expands understandings of human behavior dynamics, and helps to predict the dynamics and outcomes in advance. The optimal perception range can be solved accordingly, to practically guide the public facility planning and regular emergency training in real-life. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
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