Skip to main content

Simple Heuristics and the Modelling of Crowd Behaviours

  • Conference paper
  • First Online:
Book cover Pedestrian and Evacuation Dynamics 2012

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Helbing D, Molnar P, Farkas IJ, Bolay K (2001) Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28:361–383.

    Article  Google Scholar 

  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. Moussaïd M et al. (2012) Traffic Instabilities in Self-Organized Pedestrian Crowds. PLoS Comput Biol 8:e1002442.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Helbing D, Johansson A, Al-Abideen H (2007) The Dynamics of crowd disasters: an empirical study. Physical Review E 75:46109.

    Article  Google Scholar 

  7. Camazine S et al. (2001) Self-Organization in Biological Systems (Princeton University Press).

    Google Scholar 

  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.

    Article  Google Scholar 

  9. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:487–490.

    Article  Google Scholar 

  10. Helbing D, Keltsch J, Molnar P (1997) Modelling the evolution of human trail systems. Nature 388:47–50.

    Article  Google Scholar 

  11. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Physical Review E 51:4282–4286.

    Article  Google Scholar 

  12. Helbing D (1991) A mathematical model for the behavior of pedestrians. Behavioral Science 36:298–310.

    Article  Google Scholar 

  13. Yu WJ, Chen R, Dong LY, Dai SQ (2005) Centrifugal force model for pedestrian dynamics. Physical Review E 72:26112.

    Article  Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  17. Hoogendoorn S, Daamen W (2007) in Traffic and Granular Flow, pp 329–340.

    Google Scholar 

  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.

    Article  Google Scholar 

  19. Yu W, Johansson A (2007) Modeling crowd turbulence by many-particle simulations. Physical Review E 76:46105.

    Article  Google Scholar 

  20. Gigerenzer G, Todd P (1999) Simple Heuristics That Make Us Smart (Oxford University Press).

    Google Scholar 

  21. Gigerenzer G, Brighton H (2009) Homo Heuristicus: Why Biased Minds Make Better Inferences. Topics in Cognitive Science 1:107–143.

    Article  Google Scholar 

  22. Bennis W, Pachur T (2006) Fast and frugal heuristics in sports. Psychology of Sport and Exercise 7:611–629.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  24. Gibson J (1979) The Ecological Approach To Visual Perception (Houghton Mifflin).

    Google Scholar 

  25. Gibson JJ (1958) Visually controlled locomotion and visual orientation in animals. British Journal of Psychology 49:182–194.

    Article  Google Scholar 

  26. Hillier B, Hanson J (1984) The Social Logic of Space (Cambridge University Press).

    Google Scholar 

  27. Garling T, Garling E (1988) Distance minimization in downtown pedestrian shopping. Environment and Planning A 20:547–554.

    Article  Google Scholar 

  28. Penn A, Turner A (2002) in Pedestrian and Evacuation dynamics, eds Schreckenberg M, Sharma S (Springer), pp 99–114.

    Google Scholar 

  29. Turner A (2007) in 6th International Space Syntax Symposium, eds Kubat AS, Ertekin O, Guney YI, Eyuboglu.

    Google Scholar 

  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.

    Article  Google Scholar 

  31. Cutting JE, Vishton PM, Braren PA (1995) How we avoid collisions with stationary and with moving obstacles. Psychological Review 102:627–651.

    Article  Google Scholar 

  32. Batty M (1997) Predicting where we walk. Nature 388:19–20.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  34. Johansson A (2009) Constant-net-time headway as a key mechanism behind pedestrian flow dynamics. Physical Review E 80:26120.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  36. Steffen B (2008) in Conference proceedings of PED2008 (Springer, Berlin).

    Google Scholar 

  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.

    Article  Google Scholar 

  38. Still K (2000) Crowd Dynamics.

    Google Scholar 

  39. Daamen W, Hoogendoorn S (2002) Controlled experiments to derive walking behaviour. Journal of Transport and Infrastructure Research 3:39–59.

    Google Scholar 

  40. Hutchinson J, Gigerenzer G (2005) Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behavioural Processes 69:97–124.

    Article  Google Scholar 

  41. Nelson J, Cottrell G (2007) A probabilistic model of eye movements in concept formation. Neurocomputing 70:2256–2272.

    Article  Google Scholar 

  42. Sereno MI et al. (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268:889–893.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehdi Moussaïd .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Moussaïd, M., Nelson, J.D. (2014). Simple Heuristics and the Modelling of Crowd Behaviours. In: Weidmann, U., Kirsch, U., Schreckenberg, M. (eds) Pedestrian and Evacuation Dynamics 2012. Springer, Cham. https://doi.org/10.1007/978-3-319-02447-9_5

Download citation

Publish with us

Policies and ethics