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Evolutionary Game of Emergency Evacuation After an Earthquake at a University: How to Promote Orderly Evacuation

机译:大学地震后紧急疏散的进化游戏:如何促进有序疏散

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摘要

Safe and effective evacuation is very important to reduce casualties after earthquakes. Given that evacuation is a checking process rather than a preselection process, evolutionary game theory can be used to better understand the crowd dynamics by analyzing the imitation effects in terms of individual interactions during evacuation. The key purpose of this paper was to construct an evolutionary game model of emergency evacuation of a teaching building at a university after an earthquake to search for the specific constraints necessary to reach the expected collective behaviors for orderly evacuation. We first analyzed the evacuation scenarios, including the environment of the teaching building, emergency degree of the earthquake and behaviors of the crowd. The related game players in the evacuation process were divided into three types, impulsive evacuees, calm evacuees and university staffs, and their payoff matrix was constructed according to the evacuation scenarios. Then, an evolutionary game model via the replicated dynamic system was created, and stable strategies were discussed. Next, we simulated eighteen evacuation scenarios with adjustment of the guiding authoritativeness, emergency degree, and group-oriented coefficient; the simulation results showed that the worst stable strategies can be avoided and the expected stable strategies can be obtained by adjusting the constraints, which are closely related to the corresponding management measures. Finally, the performance of the proposed model was verified by comparisons with the real videos and the conventional strategies.
机译:安全有效的疏散对于在地震后减少伤亡非常重要。鉴于撤离是一种检查过程而不是预选过程,进化博弈论可以通过分析在疏散期间的个体相互作用方面来更好地了解人群动态。本文的关键目的是在地震发生地震后构建一个大学教学建筑的紧急疏散的进化博弈模型,以寻求达到有序疏散所需的特定限制。我们首先分析了疏散情景,包括教学建设的环境,地震和人群行为的紧急程度。疏散过程中的相关游戏玩家分为三种类型,冲动的疏散,平静的疏散和大学员工,以及根据疏散情景建造的支付矩阵。然后,创建了通过复制动态系统的进化游戏模型,讨论了稳定的策略。接下来,我们模拟了18个疏散情景,调整了指导权威,紧急程度和面向基础的系数;仿真结果表明,可以避免最稳定的策略,并且通过调整与相应的管理措施密切相关的限制,可以获得预期的稳定策略。最后,通过与真实视频和传统策略的比较验证了所提出的模型的性能。

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