Generating Emergency Evacuation Route Directions Based on Crowd Simulations with Reinforcement Learning

Unal A. E., Gezer C., Pak B. K., GÜNGÖR V. Ç.

2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022, Antalya, Turkey, 7 - 09 September 2022 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/asyu56188.2022.9925560
  • City: Antalya
  • Country: Turkey
  • Keywords: crowd simulation, deep learning, emergency evacuation, path planning, reinforcement learning
  • Kayseri University Affiliated: No


© 2022 IEEE.In an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation plan-ning 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.