Quantitative comparison between crowd models for evacuation planning and evaluation

  • Vaisagh Viswanathan
  • Chong Eu Lee
  • Michael Harold Lees
  • Siew Ann Cheong
  • Peter M. A. Sloot
Regular Article


Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.


Statistical and Nonlinear Physics 


  1. 1.
    G.K. Still, Ph.D. thesis, University of Warwick, 2000Google Scholar
  2. 2.
    S. Zhou, D. Chen, W. Cai, L. Luo, M.Y.H. Low, F. Tian, V.S.H. Tay, D.W.S. Ong, B.D. Hamilton, ACM Trans. Mod. Comput. Simul. 20, 20 (2010)Google Scholar
  3. 3.
    S. Gwynne, E. Galea, M. Owen, P. Lawrence, L. Filippidis, Building and Environment 34, 741 (1999)CrossRefGoogle Scholar
  4. 4.
    S. Regelous, K. Mannion, Massive Software – Simulating Life (2011)Google Scholar
  5. 5.
    C.W. Reynolds, Comput. Graph. 21, 25 (1987)CrossRefGoogle Scholar
  6. 6.
    J. Snape, Reciprocal collision avoidance and navigation for video games, in Game Developers Conf., San Francisco, 2012 Google Scholar
  7. 7.
    R.E. Ensemble Studios, Big Huge Games, Age of empires (2013),
  8. 8.
    D. Helbing, P. Molnár, Phys. Rev. E 51, 4282 (1995)ADSCrossRefGoogle Scholar
  9. 9.
    V. Viswanathan, M. Lees, in Transactions on Computational Science, edited by M.L. Gavrilova, K.C. Tan, C.V. Phan (Springer, 2012), pp. 1–20Google Scholar
  10. 10.
    S.J. Guy, J. Chhugani, S. Curtis, P. Dubey, M. Lin, D. Manocha, in Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aire-la-Ville, 2010, SCA ’10, pp. 119–128,
  11. 11.
    H. Klüpfel, M. Schreckenberg, T. Meyer-König, in Traffic and Granular Flow ’03, edited by S. Hoogendoorn, S. Luding, P. Bovy, M. Schreckenberg, D. Wolf (Springer, Berlin, Heidelberg, 2005), pp. 357–372Google Scholar
  12. 12.
    J.D.A. Richard D. Peacock, E.D. Kuligowski, Pedestrian And Evacuation Dynamics (Springer, 2011)Google Scholar
  13. 13.
    A. Mordvintsev, V. Krzhizhanovskaya, M. Lees, P.M.A. Sloot, Pedestrian and Evacuation Dynamics, 2012 (in press). Available at .pdf
  14. 14.
    L. Henderson, Transportation Res. 8, 509 (1974)CrossRefGoogle Scholar
  15. 15.
    J.M. Watts Jr., Fire Safety Journal 12, 237 (1987)ADSCrossRefMathSciNetGoogle Scholar
  16. 16.
    S. Paris, J. Pettré, S. Donikian, Computer Graphics Forum 26, 665 (2007)CrossRefGoogle Scholar
  17. 17.
    A Pattern-based Modeling Framework for Simulating Human-like Pedestrian Steering Behaviors, edited by S.Z. Nan Hu, Michael Lees (ACM, 2013) (to appear)Google Scholar
  18. 18.
    A. Johansson, D. Helbing, H.Z. Al-abideen, S. Al-bosta, Adv. Compl. Syst. 11, 497 (2008)CrossRefzbMATHGoogle Scholar
  19. 19.
    S. Okazaki, S. Matsushita, Engineering for Crowd Safety (Elsevier, Amsterdam, 1993)Google Scholar
  20. 20.
    J. Ondřej, J. Pettré, A.H. Olivier, S. Donikien, ACM Transactions on Graphics (TOG) 29, 123 (2010)Google Scholar
  21. 21.
    J. van den Berg, S.J. Guy, M.C. Lin, D. Manocha, in Robotics Research: The 14th International Symposium ISRR (Springer, 2011), Vol. 70Google Scholar
  22. 22.
    S.J. Guy, J. van den Berg, M.C. Lin, in Symposium on Computer Graphics, University of North Carolina, Proceedings of the 2010 annual symposium on Computational geometry, Utah, 2010, pp. 115–116Google Scholar
  23. 23.
    J. van den Berg, M.C. Lin, D. Manocha, Reciprocal Velocity Obstacles for real-time multi-agent navigation, in IEEE International Conference on Robotics and Automation, 2008. ICRA 2008, pp. 1928–1935Google Scholar
  24. 24.
    R. Hughes, Ann. Rev. Fluid Mech. 35, 169 (2003)ADSGoogle Scholar
  25. 25.
    D. Helbing, P. Mukerji, Europhys. J. Data Sci. 1, 1 (2012)Google Scholar
  26. 26.
    Simulating Complex Systems by Cellular Automata, edited by A.G. Hoekstra, J. Kroc, P.M.A. Sloot, Understanding Complex Systems (Springer, 2010)Google Scholar
  27. 27.
    S. Marconi, Ph.D. thesis, Université de Genève, 2002Google Scholar
  28. 28.
    S. Marconi, B. Chopard, in Cellular Automata, edited by S. Bandini, B. Chopard, M. Tomassini (Springer, Berlin, Heidelberg, 2002), Vol. 2493 of Lecture Notes in Computer Science, pp. 231–238Google Scholar
  29. 29.
    R. Nagai, T. Nagatani, M. Isobe, T. Adachi, Physica A 343, 712 (2004)ADSGoogle Scholar
  30. 30.
    K. Nishinari, A. Kirchner, A. Namazi, A. Schadschneider, IEICE Trans. Inform. Sys. 87, 726 (2004)Google Scholar
  31. 31.
    C.M. Henein, T. White, Crowds and Cellular Automata, Vol. 4173 of Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2006)Google Scholar
  32. 32.
    Y. Tajima, T. Nagatani, Physica A 292, 545 (2001)ADSzbMATHMathSciNetGoogle Scholar
  33. 33.
    M. Isobe, T. Adachi, T. Nagatani, Physica A 336, 638 (2004)ADSGoogle Scholar
  34. 34.
    A. Kamphuis, M.H. Overmars, in Proceedings of the 2004 ACM SIGGRAPH Symposium on Computer Animation, Utrecht University, 2004 Google Scholar
  35. 35.
    H. Xi, S. Lee, Y.J. Son, An Integrated Pedestrian Behavior Model Based on Extended Decision Field Theory and Social Force Model, in 2010 Winter Simulation Conference, 2010, pp. 824–836Google Scholar
  36. 36.
    G. Peng, X. Ruihua, Pedestrian and Evacuation Dynamics 2008 (Springer, 2010), pp. 585–595Google Scholar
  37. 37.
    D. Helbing, I. Farkas, T. Vicsek, ArXiv:0009448V1 [cond-mat.stat-mech] (2002)Google Scholar
  38. 38.
    P. Fiorini, Z. Shiller, in Proceedings. IEEE International Conference on Robotics and Automation, 1993, pp. 560–565Google Scholar
  39. 39.
    J. van den Berg, S. Patil, J. Sewall, D. Manocha, M.C. Lin, in 2008 symposium on Interactive 3D graphics and games, University of North Carolina, 2008 Google Scholar
  40. 40.
    S.J. Guy, J. Chhugani, C. Kim, N. Satish, M.C. Lin, D. Manocha, P. Dubey, in SCA ’09: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM Request Permissions, 2009 Google Scholar
  41. 41.
    S.J. Guy, M.C. Lin, D. Manocha, in 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, 2010, pp. 575–582Google Scholar
  42. 42.
    S. Luke, C. Cioffi-Revilla, L. Panait, K. Sullivan, G. Balan, Simul. Trans. Soc. Mod. Simul. Int. 82, 517 (2005)Google Scholar
  43. 43.
    X. Pan, Ph.D. thesis, Stanford University, 2006Google Scholar
  44. 44.
    J. Fruin, Public Transportation in the United States (Englewoods Cliffs, Prentice Hall, 1992)Google Scholar
  45. 45.
    D. Bauer, in Pedestrian and Evacuation Dynamics, edited by R.D. Peacock, E.D. Kuligowski, J.D. Averill (Springer, US, 2011), pp. 547–556Google Scholar
  46. 46.
    A. Seyfried, A. Schadschneider, in Cellular Automata, edited by H. Umeo, S. Morishita, K. Nishinari, T. Komatsuzaki, S. Bandini (Springer, Berlin, Heidelberg, 2008), Vol. 5191 of Lecture Notes in Computer Science, pp. 563–566Google Scholar
  47. 47.
    J. Lin, IEEE Trans. Inform. Theor. 37, 145 (1991)zbMATHGoogle Scholar
  48. 48.
    H. Abdi, D. Valentin, U.D. Bourgogne, B. Edelman, in Proceedings of the IEEE Computer Society: International Conference on Computer Vision and Pattern Recognition, 2005, pp. 42–47Google Scholar
  49. 49.
    X. Yang, Z. Wu, Natural Hazards 65, 1765 (2013)Google Scholar
  50. 50.
    X. Yang, Z. Wu, Y. Li, Physica A 390, 2375 (2011)ADSGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Vaisagh Viswanathan
    • 1
  • Chong Eu Lee
    • 2
  • Michael Harold Lees
    • 1
    • 3
    • 4
  • Siew Ann Cheong
    • 2
    • 4
  • Peter M. A. Sloot
    • 1
    • 3
    • 4
    • 5
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeRepublic of Singapore
  2. 2.Division of Physics and Applied Physics, School of Physical and Mathematical SciencesNanyang Technological UniversitySingaporeRepublic of Singapore
  3. 3.Computational ScienceUniversity of AmsterdamAmsterdamThe Netherlands
  4. 4.Complexity ProgramNanyang Technological UniversitySingaporeRepublic of Singapore
  5. 5.National Research Institute ITMOSt. PetersburgRussia

Personalised recommendations