In a crisis situation, negative emotions often spread among the crowd, and they have adverse impacts on human decisions, resulting in stampedes and crushes. Safety officers are often dispatched to scenes of emergencies because their positive emotions can calm the crowd down and avoid serious accidents. However, how to utilize the positive emotional contagion to maximize the “calm-down” effect remains a challenging problem in crowd evacuation. In this paper, we present an approach for optimizing positive emotional contagion in crowd evacuation. First, a computational model of positive emotional contagion is proposed to describe how safety officers calm a crowd down. To capture important influential factors for positive emotional contagion, such as the trust relationships among the individuals involved in a crisis situation and the variations of emotional contagion speed, we construct a trust-based emotional contagion network (Trust-ECN) and a heterogeneous emotional contagion speed computation model (HECS-CM). Based on these models, the emotional contagion process can be analyzed in a parametric way, and the infection probability for each individual in a given time window can be computed analytically with a continuous-time Markov chain (CTMC). Second, a maximization problem of emotional contagion is formulated. Since this optimization problem is NP-hard, an artificial bee colony optimized emotional contagion (ABCEC) algorithm is used to solve for the optimal positions of safety officers. We demonstrate the effectiveness of our method on both synthetic and real-world data at different scales. Finally, we implement a crowd simulation system to visualize the results of our theoretical analysis in a graphical manner. The proposed method can provide guidance for emergency response management.

中文翻译：

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在人群疏散中最佳利用积极情绪传染的策略

在危机情况下，负面情绪往往会在人群中蔓延，对人的决策产生不利影响，导致踩踏和压伤。安全人员经常被派往紧急情况现场，因为他们的积极情绪可以使人群平静下来，避免发生严重事故。然而，如何利用积极的情绪感染来最大化“冷静”效果仍然是人群疏散中的一个具有挑战性的问题。在本文中，我们提出了一种优化人群疏散中积极情绪传染的方法。首先，提出了一个积极情绪传染的计算模型来描述安全人员如何让人群平静下来。为了捕捉积极情绪传染的重要影响因素，我们构建了一个基于信任的情绪传播网络（Trust-ECN）和一个异构的情绪传播速度计算模型（HECS-CM）。基于这些模型，可以以参数方式分析情绪传染过程，并可以使用连续时间马尔可夫链（CTMC）分析计算给定时间窗口内每个人的感染概率。其次，提出了情绪传染的最大化问题。由于该优化问题是 NP-hard，因此使用人工蜂群优化情绪传染 (ABCEC) 算法来求解安全人员的最佳位置。我们证明了我们的方法在不同规模的合成和真实世界数据上的有效性。最后，我们实现了一个人群模拟系统，以图形方式可视化我们的理论分析结果。该方法可为应急响应管理提供指导。