A multi-factor coupling study of pedestrian evacuation under terrorist attacks based on a cellular automaton
构建了恐怖袭击场景下的元胞自动机疏散模型，引入恐慌量化与引导策略，发现恐慌既有激励避险减少死亡的作用，也可能引发踩踏风险，且引导策略效果因场景而异。
Terrorist attacks often lead to severe casualties and stampedes. In this paper, we constructed a pedestrian evacuation model based on cellular automata in a terrorist attack scenario, introduced the quantification mechanism of panic and guidance strategy, and analyzed the characteristics of evacuation dynamics under the dual mechanism of terrorist attack and stampede. Using the cellular automata model, the evacuation subject is divided into terrorists, ordinary pedestrians, and guiding personnel, and the motion behavior of pedestrians is simulated through the coupling of static floor field, dynamic floor field, attack threat floor field, and guiding floor field. It is found that panic has a dual role in the evacuation process: on the one hand, it can motivate pedestrians to avoid terrorist attacks and reduce the number of deaths caused by the attacks; on the other hand, it may trigger irrational behaviors and increase the risk of fatal stampede. As far as guidance strategies are concerned, the advantages and disadvantages of guidance strategies vary in different scenarios. In addition, the increase in attack intensity significantly increased the attack fatality rate, and the effect was more prominent in high crowd-density scenarios.