Studying the Impact of Trained Staff on Evacuation Scenarios by Agent-Based Simulation

  • Daniel FormoloEmail author
  • Tibor Bosse
  • Natalie van der Wal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11186)


Human evacuation experiments can trigger distress, be unethical and present high costs. As a solution, computer simulations can predict the effectiveness of new emergency management procedures. This paper applies multi-agent simulation to measure the influence of staff members with diverse training levels on evacuation time. A previously developed and validated model was extended with explicit mechanisms to simulate staff members helping people to egress. The majority of parameter settings have been based on empirical data acquired in earlier studies. Therefore, simulation results are expected to be realistic. Results show that staff are more effective in complex environments, especially when trained. Not only specialised security professionals but, especially, regular workers of shopping facilities and offices play a significant role in evacuation processes when adequately trained. These results can inform policy makers and crowd managers on new emergency management procedures.


Crowd management Evacuation Agent-based model Staff 



This project has received funding from the European Union’s Horizon 2020; innovation programme under the Marie Skłodowska-Curie grant agreement No. 748647 and Brazilian government - Science without Borders – CNPq (No: 233883/2014-2). We would like to thank our Consortium Partners and stakeholders for their input.


  1. 1.
    Canter, D.V.: Fires and Human Behaviour. Wiley, New York (1980)Google Scholar
  2. 2.
    Proulx, G.: How to initiate evacuation movement in public buildings. Facilities 17, 331–335 (1999)CrossRefGoogle Scholar
  3. 3.
    Samochine, D., Boyce, K., Shields, T.: An investigation into staff behaviour in unannounced evacuations of retail stores - implications for training and fire safety engineering. Fire Saf. Sci. 8, 519–530 (2005). Scholar
  4. 4.
    Shields, T., Boyce, K.: A study of evacuation from large retail stores. Fire Saf. J. 35, 25–49 (2000). Scholar
  5. 5.
    Gwynne, S.M.V., Purser, D., Boswell, D.L.: Pre-warning staff delay: a forgotten component in ASET/RSET calculations. In: Peacock, R.D., Kuligowski, E.D., Averill, J. (eds.) Pedestrian and Evacuation Dynamics, pp. 243–253. Springer, Boston (2011). Scholar
  6. 6.
    Smith, C.A., Ellsworth, P.C.: Patterns of cognitive appraisal in emotion. J. Pers. Soc. Psychol. 48, 813 (1985)CrossRefGoogle Scholar
  7. 7.
    de Vries, P.W., Galetzka, M., Gutteling, J.M.: Inzet communicatie bij crowd management en crowd control. Universiteit Twente-Faculteit Gedragswetenschappen (2013)Google Scholar
  8. 8.
    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. Agent. Multi-Agent Syst. 27, 52–84 (2013). Scholar
  9. 9.
    Tsai, J., et al.: ESCAPES—Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison, p. 8 (2011)Google Scholar
  10. 10.
    Pelechano, N., O’Brien, K., Silverman, B., Badler, N.: Crowd simulation incorporating agent psychological models, roles and communication. In: First International Workshop on Crowd Simulation, pp. 21–30 (2005)Google Scholar
  11. 11.
    Helbing, D., Johansson, A.: Pedestrian, crowd and evacuation dynamics. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 6476–6495. Springer, New York (2009). Scholar
  12. 12.
    Santos, G., Aguirre, B.E.: A critical review of emergency evacuation simulation models. (2004)Google Scholar
  13. 13.
    Bosse, T., Gerritsen, C., de Man, J.: An intelligent system for aggression de-escalation training. In: ECAI, pp. 1805–1811 (2016)Google Scholar
  14. 14.
    van der Wal, C.N., Formolo, D., Robinson, M.A., Minkov, M., Bosse, T.: Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements. In: Mercik, J. (ed.) Transactions on Computational Collective Intelligence XXVII. LNCS, vol. 10480, pp. 139–177. Springer, Cham (2017). Scholar
  15. 15.
    Formolo, D., van der Wal, C.N.: An adaptive simulation tool for evacuation scenarios. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds.) EPIA 2017. LNCS (LNAI), vol. 10423, pp. 766–777. Springer, Cham (2017). Scholar
  16. 16.
    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. LNCS (LNAI), vol. 10350, pp. 620–627. Springer, Cham (2017). Scholar
  17. 17.
    Rao, A.S., Georgeff, M.P., et al.: BDI agents: from theory to practice. In: ICMAS, pp. 312–319 (1995)Google Scholar
  18. 18.
    Treur, J.: Network-Oriented Modeling. Springer, Cham (2016). Scholar
  19. 19.
    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. LNCS (LNAI), vol. 10448, pp. 160–171. Springer, Cham (2017). Scholar
  20. 20.
    Challenger, R., Clegg, C.W., Robinson, M.: Understanding crowd behaviours: practical guidance and lessons identified. TSO (2010)Google Scholar
  21. 21.
    Bosse, T., Gerritsen, C., de Man, J.: Evaluation of a virtual training environment for aggression de-escalation. In: Proceedings of Game-On, pp. 48–58 (2015)Google Scholar
  22. 22.
    Kotrlik, J., Higgins, C.: Organizational research: determining appropriate sample size in survey research appropriate sample size in survey research. Inf. Technol. Learn. Perform. J. 19, 43 (2001)Google Scholar
  23. 23.
    Still, G.K.: Introduction to Crowd Science. CRC Press, Boca Raton (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceVrije UniversiteitAmsterdamThe Netherlands
  2. 2.Behavioural Science InstituteRadboud UniversityNijmegenThe Netherlands
  3. 3.Centre for Decision ResearchLeeds University Business SchoolLeedsUK

Personalised recommendations