Generating Emergency Evacuation Route Directions Based on Crowd Simulations with Reinforcement Learning
Özet
In an emergency, it is vital to evacuate individuals
from the dangerous environments. Emergency evacuation planning ensures that the evacuation is safe and optimal in terms of
evacuation time for all of the people in evacuation. To this end,
the computer-enabled evacuation simulation systems are used to
generate optimal routes for the evacuees. In this paper, a dynamic
emergency evacuation route generator has been proposed based
on indoor plans of the building and the locations of the evacuees.
To generate the optimal routes in real-time, a reinforcement
learning algorithm (proximal policy optimization) is presented.
Comparative performance results show that the proposed model
is successful for evacuating the individuals from the building in
different scenarios.
