Simulation of Crowd Dynamics in Panic Situations Using a Fuzzy Logic-Based Behavioural Model

  • Mauro Dell’Orco
  • Mario Marinelli
  • Michele Ottomanelli
Chapter
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 262)

Abstract

Tragic events in overcrowded situations have highlighted the importance of the availability of good models for pedestrian behaviour under emergency conditions. Crowd models are generally macroscopic or microscopic. In the first case, the crowd is considered to be like a fluid, so that its movement can be described through differential equations. In the second case, the collective behaviour of the crowd is the result of interactions among individual elements of the system. In this paper, we propose a microscopic model of crowd evacuation that incorporates the fuzzy perception and anxiety embedded in human reasoning. A Visual C++ application was developed to evaluate the outcomes of the model. The model was tested in scenarios with presence of a fixed obstacle. Simulation results have been analyzed in terms of door capacity and compared with an experimental study.

Keywords

Pedestrian behaviour Fuzzy logic Micro-simulation modelling Evacuation simulation 

References

  1. 1.
    Brilon, W., Großmann, M., Blanke, H.: Verfahren für die berechnung der leistungsfähigkeit und qualität des verkehrsablaufes auf straßen (Methods for the calculation of the capacity and quality of traffic flow in streets). Straßenbau und Straßenverkehrstechnik Series Number 669, Chap. 13, Ministry of Traffic, Bonn (1993)Google Scholar
  2. 2.
    Coleman, J.S., James, J.: The equilibrium size distribution of freely-forming groups. Sociometry 24, 36–45 (1961)CrossRefGoogle Scholar
  3. 3.
    Daamen, W., Hoogendoorn, S.: Capacity of doors during evacuation conditions. Procedia Eng. 3, 53–66 (2010)CrossRefGoogle Scholar
  4. 4.
    Ganem, J.: A behavioral demonstration of Fermat’s principle. Phys. Teach. 36, 76–78 (1998)CrossRefGoogle Scholar
  5. 5.
    Gipps, P.G., Marksjo, B.: A micro-simulation model for pedestrian flows. Math. Comput. Simul. 27, 95–105 (1985)CrossRefGoogle Scholar
  6. 6.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  7. 7.
    Helbing, D., Farkas, I.J., Molnar, P., Vicsek, T.: Pedestrian and Evacuation Dynamics. In: Schreckenberg, M., Sharma, S.D. (eds.) Simulation of pedestrian crowds in a normal and evacuation situations. Springer, New York (2002)Google Scholar
  8. 8.
    Helbing, D., Keltsch, J., Molnar, P.: Modelling the evolution of human trail systems. Nature 388, 47–50 (1997)CrossRefGoogle Scholar
  9. 9.
    Henderson, L.F.: The statistics of crowd fluids. Nature 229, 381–383 (1971)CrossRefGoogle Scholar
  10. 10.
    Henein, C.M., White, T.: Multi-Agent and Multi-Agent-Based Simulation: Joint Workshop MABS 2004. In: Davidsson, P., Logan, B., Takadama, K. (eds.) Agent-based modelling of forces in crowds. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Hoogendoorn, S.: Pedestrian flow by adaptive control. In: Proceedings of TRB 2004 Annual Meeting (2004)Google Scholar
  12. 12.
    IES.: Simulex Technical Reference, Evacuation modeling software. Integrated Environmental Solutions, Inc (2000)Google Scholar
  13. 13.
    Keating, J.: The myth of panic. Fire J. 77, 57–61, 147 (1982)Google Scholar
  14. 14.
    Kirchner, A., Schadschneider, A.: Simulation of evacuation process using a bionics-inspired cellular automaton model of pedestrian dynamics. Phys. A 312, 260–276 (2002)Google Scholar
  15. 15.
    Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, New Jersy (1988)MATHGoogle Scholar
  16. 16.
    Mintz, A.: Non-adaptive group behaviour. J. Abnorm. Soc. Psychol. 46(2), 150–159 (1951)Google Scholar
  17. 17.
    Nelson, H.E., MacLennan, H.A.: Handbook of Fire Protection Engineering. In: DiNenno, D.J. (ed.) Emergency movement. SFPE, Quincy (1995)Google Scholar
  18. 18.
    Okazaki, S.A.: Study of pedestrian movement in architectural space, Part 1: pedestrian movement by the application of magnetic models. Trans. Architectural Inst. Jpn. 283, 111–119 (1979)Google Scholar
  19. 19.
    Predtetschenski, W.M., Milinski, A.I.: Personenströme in Gebäuden: Berechnungsmethoden für die Projektierung (Pedestrian Flow in Buildings: Calculation Methods for Design). Müller, Köln–Braunsfeld (1971)Google Scholar
  20. 20.
    Quarantelli, E.: The behavior of panic participants. Sociol. Soc. Res. 41, 187–194 (1957)Google Scholar
  21. 21.
    Weidmann, U.: Transporttechnik der Fußgänger (Transportation technique for pedestrians). Schriftenreihe des Instituts für Verkehrsplanung, Transporttechnik, Straßen- und Eisenbahnbaunumber 90, ETH Zürich, Switzerland (1993)Google Scholar
  22. 22.
    Zadeh, L.A.: Fuzzy sets, inform. Control 8, 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  23. 23.
    Zimmermann, H.J.: Fuzzy Sets Theory—And Its Applications. Kluwer Academic Publisher, Norwell (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mauro Dell’Orco
    • 1
  • Mario Marinelli
    • 1
  • Michele Ottomanelli
    • 1
  1. 1.D.I.C.A.T.E.Ch.Technical University of BariBariItaly

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