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Simulating Crowd Evacuation with Socio-Cultural, Cognitive, and Emotional Elements

  • C. Natalie van der Wal
  • Daniel Formolo
  • Mark A. Robinson
  • Michael Minkov
  • Tibor Bosse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10480)

Abstract

In this research, the effects of culture, cognitions, and emotions on crisis management and prevention are analysed. An agent-based crowd evacuation simulation model was created, named IMPACT, to study the evacuation process from a transport hub. To extend previous research, various socio-cultural, cognitive, and emotional factors were modelled, including: language, gender, familiarity with the environment, emotional contagion, prosocial behaviour, falls, group decision making, and compliance. The IMPACT model was validated against data from an evacuation drill using the existing EXODUS evacuation model. Results show that on all measures, the IMPACT model is within or close to the prescribed boundaries, thereby establishing its validity. Structured simulations with the validated model revealed important findings, including: the effect of doors as bottlenecks, social contagion speeding up evacuation time, falling behaviour not affecting evacuation time significantly, and travelling in groups being more beneficial for evacuation time than travelling alone. This research has important practical applications for crowd management professionals, including transport hub operators, first responders, and risk assessors.

Keywords

Crowd behaviour Crowd management Crowd simulation Evacuation Emotional contagion Social dynamics Culture Cognition Group-decision making 

Notes

Acknowledgments

This research was undertaken as part of the EU HORIZON 2020 Project IMPACT (GA 653383) and Science without Borders – CNPq (scholarship reference: 233883/2014-2). We would like to thank our Consortium Partners and stakeholders for their input and the Brazilian Government.

References

  1. 1.
    Bosse, T., Duell, R., Memon, Z.A., Treur, J., van der Wal, C.N.: Agent-based modelling of emotion contagion in groups. Cog. Comp. J. 7, 111–136 (2015)CrossRefGoogle Scholar
  2. 2.
    Bosse, T., Hoogendoorn, M., Klein, M.C.A., Treur, J., van der Wal, C.N., van Wissen, A.: Modelling collective decision making in groups and crowds: integrating social contagion and interacting emotions, beliefs and intentions. Auton. Agnts. Mult. Agnt. Syst. J. 27, 52–84 (2013)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Challenger, R., Clegg, C.W., Robinson, M.A.: Understanding crowd behaviours. Practical Guidance and Lessons Identified, vol. 1. Cabinet Office, London (2010)Google Scholar
  5. 5.
    Clegg, C.W., Robinson, M.A., Davis, M.C., Bolton, L., Pieniazek, R., McKay, A.: Applying organizational psychology as a design science: a method for predicting malfunctions in socio-technical systems (PreMiSTS). Des. Sci. 3, e6 (2017)CrossRefGoogle Scholar
  6. 6.
    Damasio, A.: The Feeling of What Happens. Body and Emotion in the Making of Consciousness. Harcourt Brace, New York (1999)Google Scholar
  7. 7.
    Donald, I., Canter, D.: Intentionality and fatality during the King’s cross underground fire. Eur. J. Soc. Psychol. 22, 203–218 (1992)CrossRefGoogle Scholar
  8. 8.
    Drury, J., Cocking, C., Reicher, S.: Everyone for themselves? A comparative study of crowd solidarity among emergency survivors. Br. J. Soc. Psychol. 48(3), 487–506 (2009)CrossRefGoogle Scholar
  9. 9.
    Duives, D.C., Daamen, W., Hoogendoorn, S.P.: State-of-the-art crowd motion simulation models. Transp. Res. Part C Emerg. Technol. 37, 193–209 (2013)CrossRefGoogle Scholar
  10. 10.
    Eagly, A.H., Crowley, M.: Gender and helping behavior: a meta-analytic review of the social psychological literature. Psychol. Bull. 100(3), 283–308 (1986)CrossRefGoogle Scholar
  11. 11.
    Fang, Z., Lo, S.M., Lu, J.A.: On the relationship between crowd density and movement velocity. Fire Saf. J. 38(3), 271–283 (2003)CrossRefGoogle Scholar
  12. 12.
    Formolo, D., van der Wal, C.N.: Simulating collective evacuations with social elements. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017, Part I. LNCS, vol. 10448, pp. 160–171. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67074-4_16 CrossRefGoogle Scholar
  13. 13.
    Galea, E., Deere, S., Filippidis, L.: The safeguard validation data set - sgvds1 a guide to the data and validation procedures, fire safety engineering group. University of Greenwich (2012)Google Scholar
  14. 14.
    Grosshandler, W.L., Bryner, N., Madrzykowski, D., Kuntz, K.: Draft report of the technical investigation of The Station nightclub fire. U.S. Department of Commerce Report (2005)Google Scholar
  15. 15.
    Hall, E.T.: A system for the notation of proxemic behavior. Am. Anthropol. 65(5), 1003–1026 (1963)CrossRefGoogle Scholar
  16. 16.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39, 1–24 (2005)CrossRefGoogle Scholar
  17. 17.
    Helbing, D., Johansson, A.: Pedestrian, crowd and evacuation dynamics. In: Meyers, R.A. (ed.) Extreme Environmental Events: Complexity in Forecasting and Early Warning, pp. 697–716. Springer, New York (2011).  https://doi.org/10.1007/978-1-4419-7695-6_37 CrossRefGoogle Scholar
  18. 18.
    Hughes, H.P.N., Clegg, C.W., Robinson, M.A., Crowder, R.M.: Agent-based modelling and simulation: the potential contribution to organizational psychology. J. Occup. Organ. Psychol. 85(3), 487–502 (2012)CrossRefGoogle Scholar
  19. 19.
    Isobe, M., Helbing, D., Nagatani, T.: Experiment, theory, and simulation of the evacuation of a room without visibility. Phys. Rev. E 69(6), 066132 (2004)CrossRefGoogle Scholar
  20. 20.
    Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A 312, 260–276 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Kobes, M., Helsoot, I., De Vries, B., Post, J.G.: Building safety and human behaviour in fire: a literature review. Fire Saf. J. 45(1), 1–11 (2010)CrossRefGoogle Scholar
  22. 22.
    Liu, S., Yang, L., Fang, T., Li, J.: Evacuation from a classroom considering the occupant density around exits. Phys. A Stat. Mech. App. 388(9), 1921–1928 (2009)CrossRefGoogle Scholar
  23. 23.
    McConnell, N.C., Boyce, K.E., Shields, J., Galea, E.R., Day, R.C., Hulse, L.M.: The UK 9/11 evacuation study: analysis of survivors’ recognition and response phase in WTC1. Fire Saf. J. 45(1), 21–34 (2010)CrossRefGoogle Scholar
  24. 24.
    Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE 5(4), e10047 (2010)CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Owen, M., Galea, E.R., Lawrence, P.J.: The exodus evacuation model applied to building evacuation scenarios. J. Fire. Prot. Eng. 8(2), 65–86 (1996)CrossRefGoogle Scholar
  27. 27.
    Parisi, D.R., Dorso, C.O.: Morphological and dynamical aspects of the room evacuation process. Phys. A Stat. Mech. App. 385(1), 343–355 (2007)CrossRefGoogle Scholar
  28. 28.
    Parisi, D.R., Dorso, C.O.: Microscopic dynamics of pedestrian evacuation. Phys. A Stat. Mech. App. 354, 606–618 (2005)CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Proulx, G., Fahy, R.F.: The time delay to start evacuation: review of five case studies. Fire Saf. Sci. 5, 783–794 (1997)CrossRefGoogle Scholar
  31. 31.
    Qiu, F., Hu, X.: Modeling group structures in pedestrian crowd simulation. Sim. Mod. Pract. Th. 18(2), 190–205 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Rao, A.S., Georgeff, M.P.: BDI agents: from theory to practice. In: ICMAS, vol. 95, pp. 312–319 (1995)Google Scholar
  33. 33.
    Reininger, B.M., Raja, S.A., Carrosco, A.S., Chen, Z., Adams, B., McCormick, J., Rahbar, M.H.: Intention to comply with mandatory hurricane evacuation orders among persons living along a coastal area. Disaster Med. Publ. Health Preparedness 7(1), 46–54 (2013)CrossRefGoogle Scholar
  34. 34.
    Rizzolatti, G., Sinigaglia, C.: Mirrors in the Brain: How Our Minds Share Actions and Emotions. Oxford University Press, Oxford (2008)Google Scholar
  35. 35.
    Ronen, S., Shenkar, O.: Mapping world cultures: cluster formation, sources and implications. J. Int. Bus. Stud. 44(9), 867–897 (2013)CrossRefGoogle Scholar
  36. 36.
    Santos, G., Aguirre, B.E.: A critical review of emergency evacuation simulation models (2004)Google Scholar
  37. 37.
    Soto, C.J., John, O.P., Gosling, S.D., Potter, J.: Age differences in personality traits from 10 to 65: big five domains and facets in a large cross-sectional sample. J. Pers. Soc. Psychol. 100, 330–348 (2011)CrossRefGoogle Scholar
  38. 38.
    Still, G.K.: Introduction to Crowd Science. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  39. 39.
    Templeton, A., Drury, J., Philippides, A.: From mindless masses to small groups: conceptualizing collective behavior in crowd modeling (2015)Google Scholar
  40. 40.
    Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Switzerland (2016)CrossRefzbMATHGoogle Scholar
  41. 41.
    Tsai, J., Bowring, E., Marsella, S., Tambe, M.: Empirical evaluation of computational emotional contagion models. In: Vilhjálmsson, H.H., Kopp, S., Marsella, S., Thórisson, K.R. (eds.) IVA 2011. LNCS, vol. 6895, pp. 384–397. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23974-8_42 CrossRefGoogle Scholar
  42. 42.
    Varas, A., Cornejo, M.D., Mainemer, D., Toledo, B., Rogan, J., Munoz, V., Valdivia, J.A.: Cellular automaton model for evacuation process with obstacles. Phys. A Stat. Mech. App. 382(2), 631–642 (2007)CrossRefGoogle Scholar
  43. 43.
    van der Wal, C.N., Formolo, D., Bosse, T.: An agent-based evacuation model with social contagion mechanisms and cultural factors. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017, Part I. LNCS, vol. 10350, pp. 620–627. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60042-0_68 Google Scholar
  44. 44.
    Wei-Guo, S., Yan-Fei, Y., Bing-Hong, W., Wei-Cheng, F.: Evacuation behaviors at exit in CA model with force essentials: a comparison with social force model. Phys. A Stat. Mech. App. 371(2), 658–666 (2006)CrossRefGoogle Scholar
  45. 45.
    Wikipedia. List of countries by English speaking population (2017). https://en.wikipedia.org/wiki/List_of_countries_by_English-speaking_population
  46. 46.
    Willis, A., Gjersoe, N., Havard, C., Kerridge, J., Kukla, R.: Human movement behaviour in urban spaces: Implications for the design and modelling of effective pedestrian environments. Environ. Plann. B Plann. Des. 31(6), 805–828 (2004)CrossRefGoogle Scholar
  47. 47.
    Yuan, W.F., Tan, K.H.: An evacuation model using cellular automata. Physica A 384, 549–566 (2007)CrossRefGoogle Scholar
  48. 48.
    Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Build. Environ. 44(3), 437–445 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • C. Natalie van der Wal
    • 1
  • Daniel Formolo
    • 1
  • Mark A. Robinson
    • 2
  • Michael Minkov
    • 3
  • Tibor Bosse
    • 1
  1. 1.Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamNetherlands
  2. 2.Socio-Technical CentreLeeds University Business SchoolLeedsUK
  3. 3.Varna University of ManagementSofiaBulgaria

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