An Improved Personnel Evacuation Cellular Automata Model Based on the Ant Colony Optimization Algorithm

Abstract:

Nowadays, public safety has already attracted great attention, especially when natural disasters and other emergencies happen more and more frequently. So, personnel evacuation simulation research in the populated areas has become one of the core issues to reduce the social damage. To improve the simulation theory, this paper puts forward an improved cellular automata model using some idea of the classic Ant Colony Optimization Algorithm for reference when making rules for the evacuating personnel. And the improved model takes the interaction among the crowd and the influences exerted by the evacuating personnel upon the environment into account. The new model cares more specific details of both environment and the personnel, so it simulates the crowd psychology successfully and provides a more reliable theory that is to expand and improve the cellular automaton simulation model on personnel evacuation.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 3287-3291
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.3287
• Online since: February 2014
• Authors: Dan Qing Wang*, Qing Ge Gong, Xiao Fei Shen
• Keywords: Ant Colony Optimization Algorithm, Cellular Automata Model, Personnel Evacuation

References

[1] Xu Gao, The crowd evacuation simulation research, Southwest Jiaotong University, (2003).

[2] Tao Ping, Zhang Xiaoying, Ma Hengliang, Simulation of Personnel Evacuation Based on Cellular Automaton Model, Computer Simulation, 10-26(2009).

[3] Li Liantian, Research on Pedestrian Movement Model of Squeezed Crowd Based on Social Force, Sun Yat-sen University, (2009).

[4] The Study on the Crowds Traffic Organization in the Large-scale Activity Based on Social Force Model, Changsha University of Science & Technology, (2012).

[5] Du Ronghua, Yao Gang, Wu quanyuan, Application of an Ant Colony Algorithm in Migration of Mobile Agent, Journal of Computer Research and Development, 282-2(2007).

[6] Zhang Zhigang, The Application of Cellular Automaton in Personnel Evacuation, Shanghai University, (2009).

[7] GRANOVETTER M．Threshold models of collective behavior[J]．American Journal of Sociology, 1978: 1420－43.

[8] Hennig-Thurau, T., Groth, M., Paul, M., & Gremler D. D. (2006). Are All Smiles Created Equal? How Emotional Contagion and Emotional Labor Affect Service Relationships. Journal of Marketing, 70 (July), 58–73.

DOI: 10.1509/jmkg.70.3.58

[9] Neal, C., Quester, P., & Hawkins, D. (2004). Attitude and Attitude Change, Consumer Behaviour. Sydney: McGraw-Hill Australia Pty Limited.

[10] Ludwig J, Farley A. Using Hierarchical Dynamic Scripting to Create Adaptive Adversaries[A], In: Simulation Interoperability Standards Organization. Proceedings of 2008 Conference on Behavior Representation in Modeling and Simulation(BRIMS). NY: Curran Associates, 2008. 1-7.

[11] Hoffman, M. L. (2002). How automatic and representational is empathy, and why. Behavioral and Brain Sciences, 25, 38–39.

DOI: 10.1017/s0140525x02410011

[12] Preston, S. D., & de Waal, F. B. M. (2002). Empathy: Itsultimate and proximate bases. Behavioral and Brain Sciences, 25, 1–20.

DOI: 10.1017/s0140525x02000018