Citation

Huang P, Lin X, Liu C, Fu L, Yu L. Safety Sci. 2024; 169: e106332.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.ssci.2023.106332

PMID

unavailable

Abstract

After a fire occurs, it is imperative that people in danger evacuate as soon as possible. However, the current emergency plan based on the pre-established static exiting route is unable to considering the real-time fire scenario. In addition, the selection of evacuation routes significantly relies on the decision-maker's experiences. These issues seriously affect evacuation efficiency, decreasing the likelihood of survival. This paper developed an effective real-time evacuation guidance method that can automatically select the evacuation route in accordance with real-time fire scenarios. The model is established based on the on-policy learning algorithm SARSA (State-action-reward-state-action), an algorithm for learning a Markov decision process policy, which could mimic the decision-making of pedestrian behaviors in an emergency. In addition, two types of radar (exit radar and fire radar) are introduced into the SARSA algorithm to facilitate the wayfinding process, which formulated the so-called Radar-assisted SARSA (RSARSA). The results have shown that RSARSA can swiftly decide a safer evacuation route for pedestrians or crowd at arbitrary location. The convergence time of initial successful route planning is between 0.05 and 4.5 s under the tests in this paper. The evacuation route determined by this algorithm can well consider the fire, and timely avoid routes with potential dangerous. Moreover, RSARSA can flexibly respond to different fires under various heat release rates and development speeds. By applying this technology, fire evacuation can be guided by routes that are more attuned to the mindset of pedestrians. It can provide a good basis for route selection of crowd evacuation.

Language: en

Keywords

Evacuation; Fire emergency; On-policy Learning; Reinforcement learning; Route decision