Title: Human crowd evacuation framework and analysis using look-ahead-based reinforcement learning algorithm
Authors: Hyunsoo Lee
Addresses: School of Industrial Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi, Gyungbuk, P.O. Box 730-701, South Korea
Abstract: Human evacuation framework is one of representative applications using the digital human model. While the early human evacuation frameworks focused on the emergency modelling and evacuation simulation, contemporary evacuation models are evolved as control frameworks which can be used in real-time emergency situations. While many research studies propose several control methods for human evacuation, most of the models ignore the human crowdedness in the emergency situations, comparatively. As the ignorance of the evacuating crowdedness may lead to the more harmful situation, this paper proposes an effective method considering both objectives - the fastest evacuation routes and the crowd-less paths to the exit. In order to generate the shortest paths to the exit, a reinforcement learning approach is provided. The learning method generates candidate directions from current status, and then the less crowded directions are extracted using the proposed look-ahead crowded estimation method. The suggested method can be used as a real-time control algorithm for successful human crowd evacuations.
Keywords: human evacuation; reinforcement learning; look-ahead estimation; real-time control; evacuation control; crowd modelling; crowd evacuation; human crowds; digital human modelling; DHM; exit paths; emergency management; emergency evacuation.
International Journal of the Digital Human, 2016 Vol.1 No.3, pp.248 - 262
Available online: 18 Oct 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article