DocumentCode
1304724
Title
Modeling and Optimization of Building Emergency Evacuation Considering Blocking Effects on Crowd Movement
Author
Luh, Peter B. ; Wilkie, Christian T. ; Chang, Shi-Chung ; Marsh, Kerry L. ; Olderman, Neal
Author_Institution
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA
Volume
9
Issue
4
fYear
2012
Firstpage
687
Lastpage
700
Abstract
In building emergency evacuation, the perception of hazards can stress crowds, evoke their competitive behaviors, and trigger disorder and blocking as they pass through narrow passages (e.g., a small exit). This is a serious concern threatening evacuees\´ survivability and egress efficiency. How to optimize crowd guidance while considering such effects is an important problem. Based on advanced microscopic pedestrian models and simulations, this paper establishes a new macroscopic network-flow model where fire, smoke, and psychological factors can evoke a crowd\´s desire to escape—the desired flow rate. Disorder and blocking occur when the desired flow rate exceeds the passage capacity, resulting in a drastic decrease of crowd movement in a nonlinear and random fashion. To effectively guide crowds, a divide-and-conquer approach is developed based on groups to reduce computational complexity and to reflect psychological findings. Egress routes for individual groups are optimized by using a novel combination of stochastic dynamic programming and the rollout scheme. These routes are then coordinated so that limited passage capacities are shared to meet the total need for joint movement. Numerical testing and simulation demonstrate that, compared with a strategy of merely using nearest exits, our solution can evacuate more people more rapidly by preventing or mitigating potential disorder and blocking at bottleneck passages.
Keywords
Behavioral science; Computational modeling; Dynamic programming; Modeling; Optimization; Psychology; Simulation; Stress; Blocking effects; building emergency evacuation; crowd movement; guidance optimization; macroscopic model; psychological features;
fLanguage
English
Journal_Title
Automation Science and Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1545-5955
Type
jour
DOI
10.1109/TASE.2012.2200039
Filename
6319447
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