An Agent-Based Evacuation Model with Social Contagion Mechanisms and Cultural Factors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)


A fire incident at a transport hub can cost many lives. To save lives, effective crisis management and prevention measures need to be taken. In this project, the effect of cultural factors in managing and preventing emergencies in public transport systems is analysed. An agent–based model of an evacuating crowd was created. Socio-cultural factors that were modelled are: familiarity with environment, response time and social contagion of fear and beliefs about the situation. Simulation results show that (1) familiarity and social contagion decrease evacuation time, while increasing the number of falls; (2) crowd density and social contagion increase evacuation time and falls. All three factors show different effects on the response time. This model will be used by transport operators to estimate the effect of these socio-cultural factors and prepare for emergencies.


Crowd model Evacuation simulation Social contagion 



This research was undertaken as part of EU H2020 IMPACT GA 653383. We thank our Consortium Partners and stakeholders for their input.


  1. 1.
    Bosse, T., Hoogendoorn, M., Klein, M.C., 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. Agent. Multi-Agent Syst. 27(1), 52–84 (2013)CrossRefGoogle Scholar
  2. 2.
    Challenger, R., et al.: Understanding crowd behaviours, Volume 1: Practical guidance and lessons identified. The Stationery Office (TSO), London (2010)Google Scholar
  3. 3.
    Grosshandler, W.L., et al.: Draft report of the technical investigation of The Station nightclub fire. US Dept Comm Report (2005)Google Scholar
  4. 4.
    Kobes, M., et al.: Building safety and human behaviour in fire: a literature review. Fire Saf. J. 45(1), 1–11 (2010)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    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
  7. 7.
    Santos, G., Aguirre, B.: A critical review of emergency evacuation simulation models (2004)Google Scholar
  8. 8.
    Still, G.K.: Introduction to Crowd Science. CRC Press (2014)Google Scholar
  9. 9.
    Templeton, A., Drury, J., Philippides, A.: From mindless masses to small groups: conceptualizing collective behavior in crowd modeling (2015)Google Scholar
  10. 10.
    Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Switzerland (2016)CrossRefzbMATHGoogle Scholar
  11. 11.
    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

  1. 1.Department of Computer ScienceVrije UniversiteitAmsterdamThe Netherlands

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