Modelling Multi-Exit Large-Venue Pedestrian Evacuation With Dual-Strategy Adaptive Particle Swarm Optimization

Modeling individual and crowd behaviors is the basis for evacuation research, which is essential to reduce casualties under emergency conditions in large venues. This paper focuses on the pedestrian evacuation of large-scale venues with multiple exits, and proposes an improved pedestrian evacuation model which uses a novel dual-strategy adaptive particle swarm optimization algorithm with affinity propagation clustering. Compared with the traditional models, the proposed model is demonstrated to have a better performance in simulating pedestrians' herding behavior in panic situations, especially when not familiar with the environment, as i) individual heterogeneity is considered and affinity propagation clustering is used in population division to simulate the process of people spontaneously gathering into swarms; ii) A dual-strategy updating scheme is designed to balance the cognition difference between the leaders and the agents in the swarm; and iii) Adaptive control of parameters is used to simulate the human-surrounding interaction and psychological fluctuates when the evacuees are moving towards exits. Numerical examples, which simulated the evacuation of a rectangle venue with multiple exits, demonstrate the effectiveness and practicality of the proposed model. Moreover, the influences of pedestrian velocity, characteristics of exits, and leader movement on evacuation are analyzed. Experimental results show that the movement of leaders is different from other evacuees and the parameters of doors, such as width, quantity, and location all have a great influence on evacuation time.