Strategies to Utilize the Positive Emotional Contagion Optimally in Crowd Evacuation

• Published in: IEEE transactions on affective computing 2020-10, Vol.11 (4), p.708-721
• Main Authors: Zhang, Guijuan, Lu, Dianjie, Liu, Hong
• Format: Article
• Language: English
• Subjects: Accidents; Algorithms; Analytical models; Computational modeling; Emergency response; Emotional contagion; Emotions; Evacuation; Markov chains; Mathematical analysis; maximizing emotional contagion; Numerical models; Optimization; propagation of positive emotion; Safety; Swarm intelligence; Thermodynamics; Trustworthiness; Windows (intervals)
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2018.2836462

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.