Analysis of Empirical Trajectory Data of Pedestrians

  • Anders JohanssonEmail author
  • Dirk Helbing
Conference paper


We investigate how the characteristics and dynamics of a crowd is changing when the crowd density is increased from a few pedestrians only, up to extremely high crowd densities. Video analysis of the crowd disaster in Mina, Kingdom of Saudi-Arabia, in 2006 gives an empirical base for further analysis which reveals two transitions of the flow; one transition from laminar flow to stop-and-go flow and a second transition to turbulent flow. Finally, an improved specification of the social-force model is suggested in order to explain some of the phenomena occurring in dense crowds.


Forecast Time Crowd Density Fundamental Diagram Microscopic Simulation Tilted Line 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    D. Helbing and P. Molnár, Social force model for pedestrian dynamics, Physical Review E 51, 4282–4286 (1995). CrossRefGoogle Scholar
  2. 2.
    A. Johansson, D. Helbing, and P. K. Shukla, Specification of a microscopic pedestrian model by evolutionary adjustment to video tracking data, Advances in Complex Systems, 10, 271–288 (2007). zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    K. Teknomo, Microscopic pedestrian flow characteristics: development of an image processing data collection and simulation model, Ph.D. Dissertation, Japan (2002). Google Scholar
  4. 4.
    S. P. Hoogendoorn, W. Daamen, and P. H. L. Bovy, Extracting microscopic pedestrian characteristics from video data, in: Annual Meeting Transportation Res. Board Pre-print CD-Rom, Mira Digital Publishing, Washington, DC (2003). Google Scholar
  5. 5.
    D. Helbing, L. Buzna, A. Johansson, and T. Werner, Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions, Transportation Science 39, 1–24 (2005). CrossRefGoogle Scholar
  6. 6.
    D. Helbing, I. Farkas, and T. Vicsek, Simulating dynamical features of escape panic, Nature 407, 487–490 (2000). CrossRefGoogle Scholar
  7. 7.
    U. Weidmann, Transporttechnik der Fußgänger (Schriftenreihe des Institut für Verkehrsplanung, Transporttechnik, Straßen- und Eisenbahnbau 90, ETH Zürich, 1993). Google Scholar
  8. 8.
    J. J. Fruin, Designing for pedestrians: A level-of-service concept, Highway Research Record 355, 1–15 (1971). Google Scholar
  9. 9.
    M. Mori and H. Tsukaguchi, A new method for evaluation of level of service in pedestrian facilities, Transportation Research A 21(3), 223–234 (1987). CrossRefGoogle Scholar
  10. 10.
    A. Polus, J. L. Schofer, and A. Ushpiz, Pedestrian flow and level of service, Journal of Transportation Engineering 109, 46–56 (1983). CrossRefGoogle Scholar
  11. 11.
    A. Seyfried, B. Steffen, W. Klingsch, and M. Boltes, The fundamental diagram of pedestrian movement revisited, J. Stat. Mech., P10002 (2005). Google Scholar
  12. 12.
    D. Helbing, A. Johansson, and H. Z. Al-Abideen, The dynamics of crowd disasters: an empirical study, Physical Review E 75, 046109 (2007). CrossRefGoogle Scholar
  13. 13.
    W. Yu and A. Johansson, Modeling crowd turbulence by many-particle simulations, Physical Review E 76, 046105 (2007). CrossRefGoogle Scholar
  14. 14.
    P. K. Shukla, Modeling and simulation of pedestrians, Diploma Thesis (2005). Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Humanities and Social SciencesETH ZurichZurichSwitzerland
  2. 2.Collegium BudapestInstitute for Advanced StudyBudapestHungary

Personalised recommendations