Abstract
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.
Highlights
On July 6, 2013, Asiana Airlines Flight 214 crashed at San Francisco International Airport, which resulted in three deaths and 181 injures
To meet these strict standards, various research studies have been conducted to gauge whether airplane designs are qualified, ranging from recruiting volunteers to participate in full-scale evacuation certification demonstrations to the development of computer simulations in recent years.[7]
We propose a modified Particle Swarm Optimization algorithm (PSO) that incorporates emotion factors based on the interactions between agents to model an aircraft evacuation scenario
Summary
On July 6, 2013, Asiana Airlines Flight 214 crashed at San Francisco International Airport, which resulted in three deaths and 181 injures. [17] According to the European Transport Safety Council in 1996, there are approximately 1500 people who die each year in air transport accidents. By incorporating different states of swarm behavior, our model factors in the attraction and repulsion between agents.[5, 3] Based on these simulations, we will present a comparison of performances of passengers evacuating from different exit locations with or without the influence of emotion The goal of this project is to study how this emotion impacts individuals and the entire group as they attempt to exit the aircraft. In the previous PSO algorithm, the particles explore the designed region, compare with a fitness function, and share the location of the individual with the best position to the entire swarm at each iteration. The constant c3 controls the strength of the attraction-repulsion term
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