Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • Halpern, J.Y., (1994) A theory of knowledge and ignorance for many agents”, technical report RJ, 9894, IBM Research Division.

  • 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.

  • Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading: Addison-Wesley.

    Google Scholar 

  • Jennings, N. R. (2000). On agent-based software engineering. Artificial Intelligence, 117, 277–296.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kraus, S., Wilkenfeld, J., & Zlotkin, G. (1995). Multiagent negotiation under time constraints. Artificial Intelligence, 75(2), 297–345.

    Article  Google Scholar 

  • 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.

  • McConnell NC, Boyce KE, “Refuge areas and vertical evacuation of multistory buildings: The end users' perspectives”, fire mater. doi:10.1002/fam.2205, 2013.

  • 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.

    Article  Google Scholar 

  • 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.

  • Pankaj, M. (2012). Context-aware computing: Beyond search and location-based services. IEEE Journals & Magazines, 16, 12–16.

    Google Scholar 

  • 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.

  • 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.

  • Pires, T. T. (2005). An approach for modeling human cognitive behavior in evacuation models. Fire Safety Journal, 40(0379–7112), 177–189.

    Article  Google Scholar 

  • 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 

  • R.F.A. (2013) An Overview of the U.S. Fire Problem. NFPA’s Fire Analysis and Research Division, 1.

  • 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.

  • Sandholm, T. W., & Lesser, V. R. (1997). Coalitions among computationally bounded agents. Artificial Intelligence, 94, 99–137.

    Article  Google Scholar 

  • 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.

  • 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 

  • 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.

    Article  Google Scholar 

  • Sharma, S. (2012). Use of favorite goal in agent based modeling and simulation. IJCA, 19(1), 1–9.

  • 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.

  • 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.

  • 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.

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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 

  • 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 

  • 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 

  • 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.

  • Simulex User Guide 6.0. (2012). Integrated Environmental Solutions Limited.

  • 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.

  • Winter, H. (2012). Modeling crowd dynamics during evacuation situations using simulation. Lancaster: Lancaster University.

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Computer Science, Bowie State University, Bowie, MD, USA

    Sharad Sharma & Kola Ogunlana

  2. Army Research Laboratory, HRED, APG, Adelphi, MD, 21005, USA

    David Scribner & Jock Grynovicki

Authors
  1. Sharad Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kola Ogunlana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Scribner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jock Grynovicki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sharad Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, S., Ogunlana, K., Scribner, D. et al. Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach. Inf Syst Front 20, 741–757 (2018). https://doi.org/10.1007/s10796-017-9791-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-017-9791-x

Keywords

Search

Navigation