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Simulation and Analysis of Different Designs of Escape Areas with the Insertion of Obstacles

  • Manuela Marques Lalane Nappi
  • Ivana Righetto Moser
  • João Carlos SouzaEmail author
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Starting from Social Forces models, the homogenization of variables and fixed parameters, different architectural solutions were simulated and compared for emergency emptying of areas with large public. The results show that small adjustments in the input data generate divergent responses, indicating that the effectiveness of evacuations depends on multiple variables.

Keywords

Evacuation Social force model Design solutions 

Notes

Acknowledgements

The authors would like to thank the Post-Graduation Program in Architecture and Urbanism of Universidade Federal de Santa Catarina, the Conselho Nacional de Desenvolvimento Científico e Tecnológico [CNPq] and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [CAPES].

References

  1. 1.
    Duives, D., Daamen, W., Hoogendoorn, S.: State-of-the-art crowd motion simulation models. Transp. Res. Part C 37, 193–209 (2013)CrossRefGoogle Scholar
  2. 2.
    Escobar, R., De La Rosa, A.: Architectural design for the survival optimization of panicking fleeing victims. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, T. (eds.) Advances in Artificial Life. Proceedings of the 7th European Conference, ECAL. Lecture Notes in Computer Science, vol. 2801, pp. 97–106. Springer, Berlin, Heidelberg (2003)Google Scholar
  3. 3.
    Frank, G., Dorso, C.: Room evacuation in the presence of an obstacle. Phys. A Stat. Mech. Appl. (Amsterdam) 390(11), 213–2145 (2011)Google Scholar
  4. 4.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-Organized Pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39(1), 1–24 (2005)CrossRefGoogle Scholar
  5. 5.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  6. 6.
    Helbing, D., Farkas, I., Vicsek, T.: Crowd disasters and simulation of panic situations. In: Bunde, A., Kropp, J., Schellnhuber, H. (eds.) Science of Disaster: Climate Disruptions, Heart Attacks and Market Crashes, pp. 331–350. Springer, Berlin (2001)Google Scholar
  7. 7.
    Helbing, D., Keltsch, J., Molnár, P.: Modelling the evolution of human trail systems. Nature 388, 47–50 (1997)CrossRefGoogle Scholar
  8. 8.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)Google Scholar
  9. 9.
    Helbing, D., Farkas, I., Molnár, P., Vicsek, T.: Simulation of Pedestrian crowds in normal and evacuation situations. In: Schreckenberg, M., Sharm, S. (eds.) Pedestrian and Evacuation Dynamics, pp. 21–58. Springer, Berlin, Germany (2002a)Google Scholar
  10. 10.
    Helbing, D., Farkas, I., Vicsek, T.: Crowd Disasters and Simulation of Panic Situations. The Science of Disasters, Springer, Berlin, Heidelberg (2002b)Google Scholar
  11. 11.
    Hoogendoorn, S., Bovy, P.: Pedestrian route-choice and activity scheduling theory and models. Transp. Res. Part B Methodol. 38, 169–190 (2004)CrossRefGoogle Scholar
  12. 12.
    Illera, C., Fink, M., Hinneberg, H., Kath, K., Waldau, N., Rosic, A., Wurzer, G.: No panic. Escape and panic in buildings–architectural basic research in the context of security and safety research. In: Klingsch, W., Rogsch, C., Schadschneider, A., Schreckenberg, M. (eds.) Pedestrian and Evacuation, Dynamics, pp. 733–742. Springer, Berlin, Heidelberg (2010)Google Scholar
  13. 13.
    Jiang, L., Li, J., Shen, C., Yang, S., Han, Z.: Obstacle optimization for panic flow—reducing the tangential momentum increases the escape speed. PLoS ONE 9(12), e115463 (2014)CrossRefGoogle Scholar
  14. 14.
    Lima, F., Oliveira, D., Samed, M.: Simulação e Cenáriros como Preparação para Desastres. In: Leiras, A., Yoshizaki, H., Samed, M., Gonçalves, M. (orgs.) Logística Humanitária, 1 edn, pp. 251–272. Elsevier, Rio de Janeiro (2017)Google Scholar
  15. 15.
    Moussaïd, M., Helbing, D., Theraulaz, G.: How simple rules determine pedestrian behavior and crowd disasters. Proc. Natl. Acad. Sci. 108(17), 6884–6888 (2011)CrossRefGoogle Scholar
  16. 16.
    Shiwakoti, N., Sarvi, M.: Enhancing the panic escape of crowd through architectural design. Transp. Res. Part C Emerg. Technol. 37, 260–267 (2013)CrossRefGoogle Scholar
  17. 17.
    Shiwakoti, N., Sarvi, M., Burd, M.: Using non-human biological entities to understand pedestrian crowd behaviour under emergency conditions. Saf. Sci. 66, 1–8 (2014)CrossRefGoogle Scholar
  18. 18.
    Shukla, P.: Genetically optimized architectural designs for control of pedestrian crowds. In: Korb, K., Randall, M., Hendtlass, T. (eds.) Artificial Life: Borrowing from Biology. ACAL. Lecture Notes in Computer Science, vol. 5865, pp. 22–31. Springer, Berlin, Heidelberg (2009)Google Scholar
  19. 19.
    Yanagisawa, D., Nishi, R., Tomoeda, A., Ohtsuka, K., Kimura, A., Suma, Y., Nishinari, K.: Study on efficiency of evacuation with an obstacle on hexagonal cell space. SICE J. Control Meas. Syst. Integr. 3(6), 395–401 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Manuela Marques Lalane Nappi
    • 1
  • Ivana Righetto Moser
    • 1
  • João Carlos Souza
    • 1
    Email author
  1. 1.Universidade Federal de Santa CatarinaFlorianópolisBrazil

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