Investigating Human Factors in Fire Evacuation: A Serious-Gaming Approach

Author(s):  
K. Schatz ◽  
J. Schlittenlacher ◽  
D. Ullrich ◽  
U. Rüppel ◽  
W. Ellermeier
Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3128 ◽  
Author(s):  
Jinyue Zhang ◽  
Jianing Guo ◽  
Haiming Xiong ◽  
Xiangchi Liu ◽  
Daxin Zhang

Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.


2021 ◽  
Author(s):  
Xujie Wang ◽  
Xiaohai Zheng ◽  
Qilong Wang ◽  
Tenghui Wang

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Chen Wang ◽  
Lincoln C. Wood ◽  
Heng Li ◽  
Zhenye Aw ◽  
Abolfazl Keshavarzsaleh

Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model “Bee-Fire” using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee-Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that Bee-Fire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient.


Sign in / Sign up

Export Citation Format

Share Document