A PARTICLE SWARM OPTIMIZATION MODEL OF EMERGENCY AIRPLANE EVACUATIONS WITH EMOTION

Recent incidents such as the Asiana Flight 214 crash in San Francisco on July 6, 2013 have brought attention to the need for safer aircraft evacuation plans. In this paper we propose an emergency aircraft evacuation model inspired by Particle Swarm Optimization (PSO). By introducing an attraction-replusion force from swarm modeling we considered realistic behaviors such as feeling push-back from physical obstacles as well as reducing gaps between passengers near emergency exits. We also incorporate a scaled emotion quantity to simulate passengers experiencing fear or panic. In our model elevating emotion increases the velocity of most passengers and decreases the effect of forces exerted by nearby passengers. We also allow a small percentage of passengers to experience a sense of panic that slows their motion. Our first simulations model a Boeing 737-800 with a single class of seats that are distributed uniformly throughout the aircraft. We also simulate the evacuation of a Boeing 777-200ER with multiple service classes. We observed that increasing emotion causes most passengers to move more quickly to the exits, but that passengers experiencing panic can slow down the evacuation. Our simulations also suggest that blocking exits in locations with high seat density significantly delays the evacuation.