Understanding and Simulating Large Crowds

  • S. M. V. Gwynne
  • A. A. Siddiqui
Conference paper


Simulation tools are often used to establish pedestrian and evacuee performance. The accuracy and reliability of such tools are dependent upon their ability to qualitatively and quantitatively capture the outcome of this performance. This paper investigates the relationship between the representation of low-level agent actions and the generation of reliable emergent, high-level conditions that can then be used to better understand the conditions that may develop in large crowds and mitigate against them. Data has been collected concerning the movement of pilgrims during the Hajj. This paper presents a simple framework for categorizing these real-world observations and then translating them into the simulated environment – extracting key information from the data collected to configure the simulation tool as required. Several scenarios are simulated using the buildingEXODUS model to test the impact of representing these observations to a greater or lesser degree. This enables the importance of low-level behaviours upon emergent conditions to be investigated, even when simulating large numbers of pilgrims attending the Hajj; i.e. in large crowds. The relationship between low-level agent actions and the high-level emergent conditions is investigated using analytical and simulation tools. This paper should help future researchers (1) identify and extract key factors from crowd data and then (2) appropriately configure simulation tools to represent agent behaviour and the subsequent emergent conditions produced (e.g. congestion, flow patterns, etc.).


Completion Time Green Light Simulation Tool Travel Speed Emergent Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
  2. 2.
    S. A. AlGadhi, “Jamarat bridge: Mathematical models, computer simulation and hajjis safety analysis,” tech. rep., Ministry of Public Works and Housing - Saudi Arabia, 2003.Google Scholar
  3. 3.
    A. Johansson, “From crowd dynamics to crowd safety: a video-based analysis,” Advances in Complex Systems, vol. 4, p. 497–527, 2008.CrossRefGoogle Scholar
  4. 4.
    H. Klüpfel, “The simulation of crowd dynamics at very large events,” in Traffic and Granular Flow ’05 (A. Schadschneider, ed.), Traffic and Granular Flow ’05, Springer, 2006.Google Scholar
  5. 5.
    A. M. Shehata, “Using 3d gis to assess environmental hazards in built environments,” Journal of Al Alzhar University.Google Scholar
  6. 6.
    Z. Zainuddin, “Simulating the circumambulation of the kaaba using simwalk,” European Journal of Scientific Research, vol. 38, pp. 454–464, 2009.Google Scholar
  7. 7.
    N. A. Koshak, “Analyzing pedestrian movement in mataf using gps and gis to support space redesign,” in Proceedings of the Ninth International Conference on Design and Decision Support Systems in Architecture and Urban Planning, 2008.Google Scholar
  8. 8.
    D. Clingingsmith, “Estimating the impact of the hajj: Religion and tolerance in islam’s global gathering,” tech. rep., Harvard University, 2008. CID Working Paper No.159.Google Scholar
  9. 9.
    A. Addelghany, “Microsimulation assignment model for multidirectional pedestrian movement in congested facilities,” in Bicycles and Pedestrians; Developing Countries 2005, vol. 1939, pp. pp123–132, 2005.Google Scholar
  10. 10.
    H. Klüpfel, “The simulation of crowd dynamics at very large events calibration, empirical data, and validation,” in Proceedings of the 3rd International Conference on Pedestrian and Evacuation Dynamics, 2006.Google Scholar
  11. 11.
    S.A.H. AlGadhi, “Modelling crowd behavior and movement: Application to the makkah pilgrimage,” in Transportation and Traffic Theory, pp. 59–78, 1990.Google Scholar
  12. 12.
    N. Hussain, “Cdes: A pixel-based crowd density estimation system for masjid al-haram,” Safety Science, 2011. (Online).Google Scholar
  13. 13.
    N. Zarboutis, “Design of formative evacuation plans using agent-based simulation,” Safety Science, vol. 45, p. 920–940, 2007.CrossRefGoogle Scholar
  14. 14.
    M. Moussaïda, “How simple rules determine pedestrian behaviour and crowd disasters,” in Proceedings of the National Academy of Sciences of the United States, 2010.Google Scholar
  15. 15.
    S. Gwynne, “Simulating a building as a people movement system,” Journal of Fire Sciences, vol. 27, pp. 343–368, 2009.CrossRefGoogle Scholar
  16. 16.
    D. Vaughan, “The dark side of organizations: Mistake, misconduct, and disaster,” in Annual Review of Sociology, vol. 25, pp. 271–305, 1999.Google Scholar
  17. 17.
    E. Hollnagel, “Understanding accidents - from root causes to performance variability,” in Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants, pp. 1–6, 2002.Google Scholar
  18. 18.
    J. M. Lewis, Theories Of The Crowd: Some Cross Cultural Perspectives. Easingwold Papers, 1990. ISBN1874321043.Google Scholar
  19. 19.
    S. A. H. AlGadhi, “Simulation of crowd behavior and movement: Fundamental relations and application,” in Transportation Research Record, vol. 1320, pp. 260–268, 1991.Google Scholar
  20. 20.
    S. AlGadhi, “Review study of crowd movement and behavior,” J. of King Saud Univ, vol. 8, no. 1, pp. 77–108, 1996.Google Scholar
  21. 21.
    J. J. Fruin, Engineering For Crowd Safety, ch. The Causes And Prevention Of Crowd Disasters. 1994. 0444899200.Google Scholar
  22. 22.
    J. Drury, “Cooperation versus competition in a mass emergency evacuation: A new laboratory simulation and a new theoretical model,” Behavior Research Methods, vol. 3, no. 41, pp. 957–970, 2009.CrossRefGoogle Scholar
  23. 23.
    W. Grosshandler, “Draft report of the technical investigation into the station nightclub fire,” Tech. Rep. NCSTAR 2, National Institute of Standards and Technology, 2005.Google Scholar
  24. 24.
    J. D. Averill, “Federal investigation of the evacuation of the world trade center on september 11, 2001,” in Proceedings 3rd International Conference on Pedestrian and Evacuation Dynamics, 2005.Google Scholar
  25. 25.
    J. Bryan, The SFPE, Handbook of Fire Protection Engineering(2nd Edition), ch. Behavioural Response To Fire And Smoke, pp. (1–241)–(1–262). National Fire Protection Association, 1996.Google Scholar
  26. 26.
    H. Nelson, The SFPE, Handbook of Fire Protection Engineering(2nd Edition), ch. Emergency Movement, pp. (3–286)–(3–295). National Fire Protection Association, 1996.Google Scholar
  27. 27.
    S. A. H. AlGadhi, “A speed-concentration relation for bi-directional crowd movements with strong interaction,” in Pedestrian and Evacuation Dynamics (S. et al, ed.), pp. 3–20, 2001.Google Scholar
  28. 28.
    C. Saloma, “Self-organized queuing and scale-free behavior in real escape panic,” in Proceedings of the National Academy of Sciences of the United States, vol. 100, pp. 11947–11952, 2003.Google Scholar
  29. 29.
    C. E. Nicholson, Engineering For Crowd Safety, ch. The Investigation Of The Hillsborough Disaster By The Health And Safety Executive, pp. 361–370. Elsevier, 1994. ISBN 0444899200.Google Scholar
  30. 30.
    V. M. Predtechenskii, Planning For Foot Traffic Flow In Buildings. Amerind Publishing Co., 1978.Google Scholar
  31. 31.
    V. V. Kholshevnikova, “Recent developments in pedestrian flow theory and research in russia,” Fire Safety Journal, vol. 43, p. 108–118, 2008.CrossRefGoogle Scholar
  32. 32.
    S. M. V. Gwynne, The SFPE Handbook of Fire Protection Engineering (4th edition), ch. Employing the Hydraulic Model in Assessing Emergency Movement, p. 3–355. National Fire Protection Association, 2008.Google Scholar
  33. 33.
    J. A. . Bryan, “Selected historical review of human behavior in fire,” Fire Protection Engineering, vol. 16, pp. 4–10, 2002.Google Scholar
  34. 34.
    G. Proulx, The SFPE Handbook of Fire Protection Engineering (4th edition), ch. Evacuation Time. National Fire Protection Association, 2008.Google Scholar
  35. 35.
    J. Sorenson, “Warning and evacuation: answering some basic questions,” Industrial Crisis Quarterly, vol. 2, pp. 195–209, 1988.Google Scholar
  36. 36.
    K. Ando, “Forecasting the flow of people,” Railway Research Review, vol. 45, pp. 8–14, 1988.Google Scholar
  37. 37.
    S. M. V. Gwynne, “Optimizing fire alarm notification for high risk groups research project,” Tech. Rep., The Fire Protection Research Foundation, 2007.Google Scholar
  38. 38.
    S. M. V. Gwynne, “Conventions in the collection and use of human performance data,” Tech. Rep. NIST GCR 10–928, National Institute of Standards and Technology, 2010.Google Scholar
  39. 39.
    E. Kuligowski, “Process of human behavior in fire,” in Proceedings of the Human Behaviour in Fire Symposium, pp. 627–632, 2009.Google Scholar
  40. 40.
    E. Kuligowski, “What a user should know about selecting an evacuation model,” Fire Protection Engineering Magazine, Human Behaviour in Fire Issue, 2005.Google Scholar
  41. 41.
  42. 42.
    E. D. Kuligowski, “A review of building evacuation models, 2nd edition,” Tech. Rep. NIST TN - 1680, National Institute of Standards and Technology, 2010.Google Scholar
  43. 43.
    K. E. Boyce, “Towards the characterization of building occupants for fire safety engineering,” Fire Technology, vol. 35, no. 1, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Hughes AssociatesLondonUK
  2. 2.Fire Safety Engineering GroupUniversity of GreenwichGreenwichUK

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