Title
Crowd evacuation planning using cartesian genetic programming and agent-based crowd modeling.
Abstract
This paper proposes a new evolutionary algorithm-based methodology for optimal crowd evacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic Programming algorithm and an agent-based crowd simulation model is developed to search for the optimal heuristic rule. By considering dynamic environment features to construct the heuristic rule and using multiple scenarios for training, the proposed methodology aims to find generic and efficient heuristic rules that perform well on different scenarios. The proposed methodology is applied to guide people's evacuation behaviors in six different scenarios. The simulation results demonstrate that the heuristic rule offered by the proposed method is effective to reduce the crowd evacuation time on different scenarios.
Year
DOI
Venue
2015
10.1109/WSC.2015.7408158
Winter Simulation Conference
Keywords
Field
DocType
agent-based crowd modeling,evolutionary algorithm-based methodology,optimal crowd evacuation planning,Cartesian genetic programming algorithm,agent-based crowd simulation model,optimal heuristic rule
Heuristic,Evolutionary algorithm,Computer science,Simulation,Crowd evacuation,Evolutionary computation,Genetic programming,Cartesian genetic programming,Artificial intelligence,Crowd simulation,Evolutionary programming,Machine learning
Conference
ISSN
ISBN
Citations 
0891-7736
978-1-4673-9741-4
2
PageRank 
References 
Authors
0.42
12
3
Name
Order
Citations
PageRank
Jing-hui Zhong138033.00
Wentong Cai21928197.81
Linbo Luo3537.54