Information Systems Frontiers

, Volume 20, Issue 4, pp 741–757 | Cite as

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

  • Sharad SharmaEmail author
  • Kola Ogunlana
  • David Scribner
  • Jock Grynovicki


It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.


Human behavior Modeling emergency Agent-based modeling Simulation Fuzzy logic Behavior simulation 



This work is funded in part by the National Science Foundation grant number HRD-1238784. The authors would also like to acknowledge the TMCF (Thurgood Marshall College Funds) faculty fellowship for the support.


  1. Averill, J. D., Mileti, D., Peacock, R., Kuligowski, E., Groner, N., Proulx, G., Reneke, P., & Nelson, H. (2012). Federal investigation of the evacuation of the world trade center on September 11, 2001. Fire and Materials, 36(5–6), 472–480.CrossRefGoogle Scholar
  2. Bates, J. (1994). The role of emotion in believable agents. Communications of the ACM Special Issue on Intelligent Agents, 37(7), 122–125.Google Scholar
  3. Benthorn, L., & Frantzich, H. (1996). Fire alarm in a public building: How do people evaluate information and choose evacuation exit? Dept of Fire Safety Engineering: Lund University.Google Scholar
  4. Choi, S.H., & Zhu, W.K., (2012) Performance optimization of mobile robots in dynamic environments. Virtual environments human-computer interfaces and measurements systems (VECIMS) 2012 I.E. international conference, 1–3.Google Scholar
  5. Chooramun, N., Lawrence, P., & Gale, E. (2010). Implementing a hybrid space discretisation within an agent based evacuation model. Maryland USA: PED 2010, NIST.Google Scholar
  6. Gershon, R. R. M., Magda, L. A., Riley, H. E. M., & Sherman, M. F. (2012). The world trade center evacuation study: Factors associated with initiation and length of time for evacuation. Fire and Materials, 36(5–6), 481–500. doi: 10.1002/Fam.1080.CrossRefGoogle Scholar
  7. Gwynne, E. R. S., Lawrence, P. J., & Filippidis, L. (n.d.). A review of the methodologies used in the computer simulation of evacuation from the built environment. In Fire safety engineering group, Center for Numerical Modeling and Process Analysis. London: University of Greenwich.Google Scholar
  8. Halpern, J.Y., (1994) A theory of knowledge and ignorance for many agents”, technical report RJ, 9894, IBM Research Division.Google Scholar
  9. Helbing, D., Farkas, I., Molnar, P., & Vicsek, T. (2002). Simulation of pedestrian crowds in normal and evacuation simulations. In: Schreckenberg, M. and Sharma, S. D. (Eds.), Pedestrian and Evacuation Dynamics (pp., 21–58). Berlin: Springer.Google Scholar
  10. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading: Addison-Wesley.Google Scholar
  11. Jennings, N. R. (2000). On agent-based software engineering. Artificial Intelligence, 117, 277–296.CrossRefGoogle Scholar
  12. Kinateder, M., Ronchi, E., Gromer, D., Müller, M., Jost, M., Nehfischer, M., Mühlberger, A., & Pauli, P. (2014). Social influence on route choice in a virtual reality tunnel fire. Transportation Research Part F: Traffic Psychology and Behaviour, 6, 116–125.CrossRefGoogle Scholar
  13. Kraus, S., Wilkenfeld, J., & Zlotkin, G. (1995). Multiagent negotiation under time constraints. Artificial Intelligence, 75(2), 297–345.CrossRefGoogle Scholar
  14. P.C.I. Mae, Y.K Ohara, & Arai, T. (2012) “Social human behavior modeling for robot imitation learning”. Mechatronics and Automation (ICMA), 2012 International Conference, 457.Google Scholar
  15. McConnell NC, Boyce KE, “Refuge areas and vertical evacuation of multistory buildings: The end users' perspectives”, fire mater. doi: 10.1002/fam.2205, 2013.
  16. McConnell, N. C., Boyce, K. E., Shields, J., Galea, E. R., Day, R. C., & Hulse, L. M. (2010). The UK 9/11 evacuation study: Analysis of survivors recognition and response phase in WTC1. Fire Safety Journal, 45(1), 21–34. doi: 10.1016/j.firesaf.2009.09.001.CrossRefGoogle Scholar
  17. Ogunlana, K., Sharma, S., (2014) Agent based simulation model for data visualization during evacuation, proceedings of 2014 ASE/IEEE BIGDATA/SOCIALCOM/CYBERSECURITY conference, ISBN: 978-1-62561-000-3, pp. 1–6, Stanford University.Google Scholar
  18. Pankaj, M. (2012). Context-aware computing: Beyond search and location-based services. IEEE Journals & Magazines, 16, 12–16.Google Scholar
  19. Pant, T.R.M., Chelliah, T., & Abraham, A. (2012) “opposition based chaotic differential evolution algorithm for solving global optimization problems”. Nature and biologically inspired computing (NaBIC), 2912 fourth world congress, 1.Google Scholar
  20. Peacock, R. D., Averill, J. D., & Kuligowski, E. D. (2009). Stairwell evacuation from buildings: What we know we Don’t know. National Institute of Standards and Technology, Gaithersburg, 16.Google Scholar
  21. Pires, T. T. (2005). An approach for modeling human cognitive behavior in evacuation models. Fire Safety Journal, 40(0379–7112), 177–189.CrossRefGoogle Scholar
  22. Quinn, M. J., Metoyer, R. A., & Hunter-Zaworski, K. (2003). Parallel implementation of the social forces model. USA: School of Electrical Engineering and Computer Science Department of civil, construction, and environmental engineering Oregon State University Corvallis, OR 97331.Google Scholar
  23. R.F.A. (2013) An Overview of the U.S. Fire Problem. NFPA’s Fire Analysis and Research Division, 1.Google Scholar
  24. Rao, A. S., & Georgeff, M. P. (1995). “BDI agents: From theory to practice”, in victor lesser, editor, proceedings of the first international conference on multi–agent systems, MIT press, pages 312–319. San Francisco.Google Scholar
  25. Sandholm, T. W., & Lesser, V. R. (1997). Coalitions among computationally bounded agents. Artificial Intelligence, 94, 99–137.CrossRefGoogle Scholar
  26. Scerri, D., Hickmott, S., & Padgham, L. (2012). "User understanding of cognitive processes in simulation: A tool for exploring and modifying," Proceedings of the 2012 Winter Simulation Conference (WSC), Berlin, pp. 1–12. doi: 10.1109/WSC.2012.6465046.
  27. Sharma, S. (2009). Avatarsim: A multi-agent system for emergency evacuation simulation. Journal of Computational Methods in Science and Engineering, 9(1,2), S13–S22, ISSN 1472–7978.Google Scholar
  28. Sharma, S. (2010). “Fuzzy approach for predicting probability of reaching a target in a battlefield environment”, international journal of computers and their applications. IJCA, 17(1), 16–24.CrossRefGoogle Scholar
  29. Sharma, S. (2012). Use of favorite goal in agent based modeling and simulation. IJCA, 19(1), 1–9.Google Scholar
  30. Sharma, S., and Ogunlana, K., (2013) Using genetic algorithm and neural networks in a goal finding application for evacuation, proceedings at the ISCA 22nd international conference on software engineering and data engineering (SEDE-2013), Los Angeles, pp. 25-30.Google Scholar
  31. Sharma, S., and Ogunlana, K., (2015a) Using Genetic Algorithm & Neural Network for modeling learning behavior in a multi-agent system during emergency evacuation, extended paper from proceedings at the ISCA 30th international conference on computers and their applications (CATA 2015), Honolulu 9–11.Google Scholar
  32. Sharma, S., and Ogunlana, K., (2015b) Modeling learning behavior in a multi-agent system using GA & NN during evacuation, proceedings at the ISCA 30th international conference on computers and their applications (CATA 2015), Honolulu, 9–11.Google Scholar
  33. Sharma, S., & Ogunlana, K. (2015c). Using genetic algorithm and neural network for modeling learning behavior in a multi-agent system during emergency evacuation. International Journal of Computers and their Applications, IJCA, 22(4), 172–182.Google Scholar
  34. Sharma, S., & Singh, H. (2006). Multi-agent system for simulating human behavior in egress simulations, proceedings of NAACSOS, annual conference of the north American Association for Computational Social and Organizational Sciences. Indiana: Notre Dame.Google Scholar
  35. Sharma, S., Singh, H., & Gerhart, G. R. (2007). Simulation of convoy of unmanned vehicles using agent based modeling. SPIE conference on security and defense, Florence, Italy, 6736, 17–20.Google Scholar
  36. Sharma, S., Singh, H., & Prakash, A. (2008). Multi-agent modeling and simulation of human behavior in aircraft evacuations. IEEE Transactions on Aerospace and Electronic Systems, 44(4), 1477–1488.CrossRefGoogle Scholar
  37. Sharma, S., Otunba, S., Ogunlana, K., & Tripathy, T. (2012). Intelligent agents in a goal finding application for homeland security. In Proceedings of the IEEE Southeastcon (p. 1–5). Orlando: IEEE.Google Scholar
  38. Shehory, O., & Kraus, S. (1996). A kernel-oriented model for coalition-formation in general environments: Implementation and results. In Proc. of AAAI-96 (pp. 134–140). Oregon: Portland.Google Scholar
  39. Shen, T., & Chien, S. (2005). An Evacuation Simulation Model (Esm) For Building Evaluation. Graduate School of Fire Science and Administration, Central Police University, Taiwan International Journal on Architectural Science, 6(1), 15–30.Google Scholar
  40. Shi, L., Xie, Q., Cheng, X., Chen, L., Zhou, Y., & Zhang, R. (2009). Developing a database for emergency evacuation model. State key Laboratory of Fire Science. West Campus, Anhui: University of Science and Technology of China.Google Scholar
  41. Shoham, Y. and Tennenholtz, M., (1992) On the synthesis of useful social laws for artificial agent societies, in proc. of AAAI-92, pages 276–281, California.Google Scholar
  42. Simulex User Guide 6.0. (2012). Integrated Environmental Solutions Limited.Google Scholar
  43. Van Troi Tran (2013) More than just another crowd, we need a waiting line instead”. Distinktion: Scandinavian Journal of Social Theory, 14(2), Special Issue: Postmodern Crowds: Re-Inventing Crowd Thinking.Google Scholar
  44. Winter, H. (2012). Modeling crowd dynamics during evacuation situations using simulation. Lancaster: Lancaster University.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Sharad Sharma
    • 1
    Email author
  • Kola Ogunlana
    • 1
  • David Scribner
    • 2
  • Jock Grynovicki
    • 2
  1. 1.Department of Computer ScienceBowie State UniversityBowieUSA
  2. 2.Army Research Laboratory, HRED, APGAdelphiUSA

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