Simple Heuristics and the Modelling of Crowd Behaviours

Conference paper


A crowd of pedestrians is a complex system that exhibits a rich variety of self-organized collective behaviors, such as lane formation, stop-and-go waves, or crowd turbulence. Understanding the mechanisms of crowd dynamics requires establishing a link between the local behavior of pedestrians during interactions, and the global dynamics of the crowd at high density. For this, the elaboration of a model is necessary.

In this contribution, we will make a distinction between two kinds of modelling methods: outcome models that are often based on analogies with Newtonian mechanics, and process models based on concepts of cognitive science. While outcome models describe directly the movements of a pedestrian by means of repulsive forces or probabilities to move from one place to another, process models generate the movement from the bottom-up by describing the underlying cognitive process used by the pedestrian during navigation.

Here, we will describe and compare two representatives of outcome and process models, namely the social force model on the one hand, and the heuristic model on the other hand. In particular, we will describe the strength and the limitations of each approach, and discuss possible future improvements for process models.


Outcome models Process models Pedestrian behaviour Crowd dynamics Complex systems Social forces Simple heuristics 


  1. 1.
    Helbing D, Molnar P, Farkas IJ, Bolay K (2001) Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28:361–383.CrossRefGoogle Scholar
  2. 2.
    Yamori K (1998) Going with the flow : Micro-macro dynamics in the macrobehavioral patterns of pedestrian crowds. Psychological review 105:530–557.Google Scholar
  3. 3.
    Moussaïd M et al. (2012) Traffic Instabilities in Self-Organized Pedestrian Crowds. PLoS Comput Biol 8:e1002442.CrossRefGoogle Scholar
  4. 4.
    Kretz T, Grünebohm A, Kaufman M, Mazur F, Schreckenberg M (2006) Experimental study of pedestrian counterflow in a corridor. Journal of Statistical Mechanics: Theory and Experiment 2006:P10001–P10001.CrossRefGoogle Scholar
  5. 5.
    Helbing D, Buzna L, Johansson A, Werner T (2005) Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions. Transportation Science 39:1–24.Google Scholar
  6. 6.
    Helbing D, Johansson A, Al-Abideen H (2007) The Dynamics of crowd disasters: an empirical study. Physical Review E 75:46109.CrossRefGoogle Scholar
  7. 7.
    Camazine S et al. (2001) Self-Organization in Biological Systems (Princeton University Press).Google Scholar
  8. 8.
    Moussaïd M, Perozo N, Garnier S, Helbing D, Theraulaz G (2010) The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics. PLoS ONE 5:e10047.CrossRefGoogle Scholar
  9. 9.
    Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:487–490.CrossRefGoogle Scholar
  10. 10.
    Helbing D, Keltsch J, Molnar P (1997) Modelling the evolution of human trail systems. Nature 388:47–50.CrossRefGoogle Scholar
  11. 11.
    Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Physical Review E 51:4282–4286.CrossRefGoogle Scholar
  12. 12.
    Helbing D (1991) A mathematical model for the behavior of pedestrians. Behavioral Science 36:298–310.CrossRefGoogle Scholar
  13. 13.
    Yu WJ, Chen R, Dong LY, Dai SQ (2005) Centrifugal force model for pedestrian dynamics. Physical Review E 72:26112.CrossRefGoogle Scholar
  14. 14.
    Johansson A, Helbing D, Shukla P (2007) Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems 10:271–288.CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Moussaïd M et al. (2009) Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society B: Biological Sciences 276:2755–2762.CrossRefGoogle Scholar
  16. 16.
    Johansson A, Helbing D, Al-Abideen HZ, Al-Bosta S (2008) From crowd dynamics to crowd safety: A video-based analysis. Advances in Complex Systems 11:479–527.CrossRefGoogle Scholar
  17. 17.
    Hoogendoorn S, Daamen W (2007) in Traffic and Granular Flow, pp 329–340.Google Scholar
  18. 18.
    Helbing D, Johansson A, Mathiesen J, Jensen M, Hansen A (2006) Analytical Approach to Continuous and Intermittent Bottleneck Flows. Physical Review Letters 97:168001.CrossRefGoogle Scholar
  19. 19.
    Yu W, Johansson A (2007) Modeling crowd turbulence by many-particle simulations. Physical Review E 76:46105.CrossRefGoogle Scholar
  20. 20.
    Gigerenzer G, Todd P (1999) Simple Heuristics That Make Us Smart (Oxford University Press).Google Scholar
  21. 21.
    Gigerenzer G, Brighton H (2009) Homo Heuristicus: Why Biased Minds Make Better Inferences. Topics in Cognitive Science 1:107–143.CrossRefGoogle Scholar
  22. 22.
    Bennis W, Pachur T (2006) Fast and frugal heuristics in sports. Psychology of Sport and Exercise 7:611–629.CrossRefGoogle Scholar
  23. 23.
    Moussaïd M, Helbing D, Theraulaz G (2011) How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Science 108:6884–6888.CrossRefGoogle Scholar
  24. 24.
    Gibson J (1979) The Ecological Approach To Visual Perception (Houghton Mifflin).Google Scholar
  25. 25.
    Gibson JJ (1958) Visually controlled locomotion and visual orientation in animals. British Journal of Psychology 49:182–194.CrossRefGoogle Scholar
  26. 26.
    Hillier B, Hanson J (1984) The Social Logic of Space (Cambridge University Press).Google Scholar
  27. 27.
    Garling T, Garling E (1988) Distance minimization in downtown pedestrian shopping. Environment and Planning A 20:547–554.CrossRefGoogle Scholar
  28. 28.
    Penn A, Turner A (2002) in Pedestrian and Evacuation dynamics, eds Schreckenberg M, Sharma S (Springer), pp 99–114.Google Scholar
  29. 29.
    Turner A (2007) in 6th International Space Syntax Symposium, eds Kubat AS, Ertekin O, Guney YI, Eyuboglu.Google Scholar
  30. 30.
    Ondřej J, Pettré J, Olivier A-H, Donikian S (2010) A synthetic-vision based steering approach for crowd simulation. ACM Trans Graph 29:1–9.CrossRefGoogle Scholar
  31. 31.
    Cutting JE, Vishton PM, Braren PA (1995) How we avoid collisions with stationary and with moving obstacles. Psychological Review 102:627–651.CrossRefGoogle Scholar
  32. 32.
    Batty M (1997) Predicting where we walk. Nature 388:19–20.CrossRefGoogle Scholar
  33. 33.
    Turner A, Penn A (2002) Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment. Environment and Planning B: Planning and Design 29:473–490.CrossRefGoogle Scholar
  34. 34.
    Johansson A (2009) Constant-net-time headway as a key mechanism behind pedestrian flow dynamics. Physical Review E 80:26120.CrossRefMathSciNetGoogle Scholar
  35. 35.
    Ballerini M et al. (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences 105:1232.CrossRefGoogle Scholar
  36. 36.
    Steffen B (2008) in Conference proceedings of PED2008 (Springer, Berlin).Google Scholar
  37. 37.
    Ma J, Song W, Zhang J, Lo S, Liao G (2010) k-nearest-neighbor interaction induced self-organized pedestrian counter flow. Physica A 389:2101–2117.CrossRefGoogle Scholar
  38. 38.
    Still K (2000) Crowd Dynamics.Google Scholar
  39. 39.
    Daamen W, Hoogendoorn S (2002) Controlled experiments to derive walking behaviour. Journal of Transport and Infrastructure Research 3:39–59.Google Scholar
  40. 40.
    Hutchinson J, Gigerenzer G (2005) Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behavioural Processes 69:97–124.CrossRefGoogle Scholar
  41. 41.
    Nelson J, Cottrell G (2007) A probabilistic model of eye movements in concept formation. Neurocomputing 70:2256–2272.CrossRefGoogle Scholar
  42. 42.
    Sereno MI et al. (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268:889–893.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Center for Adaptive Behavior and CognitionMax Planck Institute for Human DevelopmentBerlinGermany

Personalised recommendations