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Knowledge and emotion dual-driven method for crowd evacuation
Knowledge-Based Systems ( IF 7.2 ) Pub Date : 2020-09-17 , DOI: 10.1016/j.knosys.2020.106451
Zena Tian , Guijuan Zhang , Chunyu Hu , Dianjie Lu , Hong Liu

Knowledge and emotion play important roles in the process of crowd evacuation. Knowledge determines the moving direction of an individual, and emotion determines the moving speed of an individual. However, the existing works lack consideration of knowledge and emotion at the same time, which results in unreasonable simulation of crowd evacuation. To solve this problem, we propose a knowledge and emotion dual-driven method for crowd evacuation simulation. First, the knowledge representation and knowledge transmission are modeled to quantify the knowledge of evacuees. In this step, the knowledge representation is modeled by differentiating the hazard source knowledge and the scenario knowledge and the factors affecting knowledge transmission are combined with the susceptible infected (SI) model to simulate the process of knowledge transmission. Second, the emotion model is constructed to quantify the individual emotion in the evacuation process. In this paper, the quantification of emotion takes into account two factors: (1) the influence of individual knowledge on emotion is quantified by using Siminov’s psychology model; (2) the influence of emotional contagion among individuals is also considered. Then, quantitative individual knowledge and emotion are combined with the reciprocal velocity obstacles (RVO) model to build the knowledge and emotion dual-driven crowd evacuation model. Finally, we implement a 3D crowd simulation system based on the knowledge and emotion dual-driven model to visualize the results of our theoretical analysis. The experimental results show that the proposed method can more realistically simulate crowd evacuation in an emergency situation.



中文翻译:

知识与情感双重驱动的人群疏散方法

知识和情感在人群疏散过程中起着重要作用。知识决定个体的运动方向,情感决定个体的运动速度。然而,现有作品缺乏对知识和情感的同时考虑,导致人群疏散模拟不合理。为了解决这个问题,我们提出了一种知识和情感双重驱动的人群疏散模拟方法。首先,对知识表示和知识传递进行建模以量化撤离者的知识。在这一步骤中,通过区分危险源知识和情景知识对知识表示进行建模,并将影响知识传播的因素与易受感染的(SI)模型相结合,以模拟知识传播的过程。其次,构建情感模型以量化疏散过程中的个人情感。在本文中,情感的量化考虑了两个因素:(1)使用西米诺夫的心理学模型来量化个人知识对情感的影响。(2)还考虑了情绪传染在个体之间的影响。然后,将定量的个人知识和情感与往复速度障碍物(RVO)模型相结合,以建立知识和情感双重驱动的人群疏散模型。最后,我们基于知识和情感双重驱动模型实现了3D人群仿真系统,以可视化我们的理论分析结果。实验结果表明,该方法可以更真实地模拟紧急情况下的人群疏散。建立情绪模型以量化疏散过程中的个人情绪。在本文中,情感的量化考虑了两个因素:(1)使用西米诺夫的心理学模型来量化个人知识对情感的影响。(2)还考虑了情绪传染在个体之间的影响。然后,将定量的个人知识和情感与往复速度障碍物(RVO)模型相结合,以建立知识和情感双重驱动的人群疏散模型。最后,我们基于知识和情感双重驱动模型实现了3D人群仿真系统,以可视化我们的理论分析结果。实验结果表明,该方法可以更真实地模拟紧急情况下的人群疏散。建立情绪模型以量化疏散过程中的个人情绪。在本文中,情感的量化考虑了两个因素:(1)使用西米诺夫的心理学模型来量化个人知识对情感的影响。(2)还考虑了情绪传染在个体之间的影响。然后,将定量的个人知识和情感与往复速度障碍物(RVO)模型相结合,以建立知识和情感双重驱动的人群疏散模型。最后,我们基于知识和情感双重驱动模型实现了3D人群仿真系统,以可视化我们的理论分析结果。实验结果表明,该方法可以更真实地模拟紧急情况下的人群疏散。

更新日期:2020-09-20
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