In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.
By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.
The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.
In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.
This research is supported by the National Natural Science Foundation of China (No. 72071212, No. 72204093), Ministry of Education of Humanities and Social Science Project (No. 21YJC630118), Natural Science Foundation of Hubei Province (No. 2020CFB518, 2020CFB321) and the Fundamental Research Funds for the Central Universities (No. 2662019QD025).
Song, Y., Liu, B., Li, L. and Liu, J. (2022), "Modelling and simulation of crowd evacuation in terrorist attacks", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-02-2022-0260
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