Skip to main content
SpringerLink
Log in

A Data-Driven Model of Pedestrian Following and Emergent Crowd Behavior

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

Abstract

Pedestrian following is a common behavior, and may provide a key link between individual locomotion and crowd dynamics. Here, we present a model for following that is motivated by cognitive science and grounded in empirical data. In Experiment 1, we collected data from leader-follower pairs, and showed that a simple speed-matching model is sufficient to account for behavior. In Experiment 2, we manipulated the visual information of a virtual leader, and found that followers respond primarily to changes in visual angle.

Finally, in Experiment 3, we use the speed-matching model to simulate speed coordination in small crowds of four pedestrians. The model performs as well in these small crowds as it did in the leader-follower pairs. This cognitively-inspired, empirically-grounded model can serve as a component in a larger model of crowd dynamics.

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

Access this chapter

Log in via an institution
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. Goldstone, R.L., Gureckis, T.M.: Collective Behavior. Top. Cogn. Sci. 1(3), 412–438 (2009)

    Article  Google Scholar 

  2. Reynolds, C.W.: Flocks, Herds, and Schools: A Distributed Behavioral Model. Comp. Graph. 21(4), 25–34 (1987)

    Article  Google Scholar 

  3. Helbing, D., Molnár., P.: Social Force Model for Pedestrian Dynamics. Phys. Rev. E. 51(5), 4282–4286 (1995)

    Google Scholar 

  4. Helbing, D., Farkas, I., Vicsek, T.: Simulating Dynamical Features of Escape Panic. Nature 407(6803), 487–490 (2000)

    Article  Google Scholar 

  5. Piccoli, B., Tosin, A.: Pedestrian Flows in Bounded Domains with Obstacles. Continuum Mech. Therm. 21(2), 85–107 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  6. Muramatsu, M., Irie, T., Nagatani, T.: Jamming Transition in Pedestrian Counter Flow. Physica A, 267(3–4), 487–498 (1999)

    Article  Google Scholar 

  7. Portz, A., Seyfried, A.: Modeling Stop-and-Go Waves in Pedestrian Dynamics. In: Wryzkowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) LNCS, vol. 6068, pp. 561–568. Springer, Heidelberg (2010)

    Google Scholar 

  8. Yu, W., Johansson, A.: Modeling Crowd Turbulence by Many-Particle Simulations. Phys. Rev. E 76(4), 046105 (2007)

    Article  Google Scholar 

  9. Ondřej, J., Pettré, J., Olivier, A.-H., Donikian, S.: A Synthetic-Vision Based Steering Approach for Crowd Simulation. ACM T. Graphic. 29(4), 123 (2010)

    Google Scholar 

  10. Moussaïd, M., Helbing, D., Theraulaz, G.: How Simple Rules Determine Pedestrian Behavior and Crowd Disasters. Proc. Natl. Acad. Sci. USA 108(17), 6884–6888 (2011)

    Article  Google Scholar 

  11. Lakoba, T.I., Kaup, D.J., Finkelstein, N.M.: Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution. Simulation 81(5), 339–352 (2005)

    Article  Google Scholar 

  12. Henderson, L.F.: The Statistics of Crowd Fluids. Nature 229, 381–383 (1971)

    Article  Google Scholar 

  13. Kretz, T., Grünebohm, A., Schreckenberg, M.: Experimental Study of Pedestrian Flow through a Bottleneck. J. Stat. Mech. P10014 (2006)

    Google Scholar 

  14. Moussaïd, M., Helbing, D., Garnier, S., Johansson. A., Combe, M., Theraulaz, G.: Experimental Study of the Behavioral Mechanisms Underlying Self-Organization in Human Crowds. P. Roy. Soc. B. 276(1688), 2755–2762 (2009)

    Google Scholar 

  15. Robin, T., Antonini, G., Bierlaire, M., Cruz, J.: Specification, Estimation, and Validation of a Pedestrian Walking Behavior Model. Transport. Res. B. 43(1), 36–56 (2009)

    Article  Google Scholar 

  16. Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics. PLoS One 5(4), e10047 (2010)

    Article  Google Scholar 

  17. Schadschneider, A., Seyfried, A.: Empirical Results for Pedestrian Dynamics and Their Implications for Modeling. Netw. Heterog. Media 6(3), 545–560 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  18. Warren, W.H.: The Dynamics of Perception and Action. Psychol. Rev. 113(2), 358–389 (2006)

    Article  MathSciNet  Google Scholar 

  19. Gibson, J.J.: The Ecological Approach to Visual Perception. Psychology Press, New York (1979)

    Google Scholar 

  20. Kugler, P., Turvey, M.: Information, Natural Law, and the Self-Assembly of Rhythmic Movement: Resources for Ecological Psychology. Erlbaum, Hillsdale (1987)

    Google Scholar 

  21. Kelso, S.: Dynamic Patterns: The Self-Organization of Brain and Behavior (Complex Adaptive Systems). The MIT Press, Cambridge (1995)

    Google Scholar 

  22. Fajen, B.R., Warren, W.H.: Behavioral Dynamics of Steering, Obstacle Avoidance, and Route Selection. J. Exp. Psychol. Human 29(2), 343–362 (2003)

    Article  Google Scholar 

  23. Fajen, B.R., Warren, W.H.: Behavioral Dynamics of Intercepting a Moving Target. Exp. Brain Res. 180(2), 303–319 (2007)

    Article  Google Scholar 

  24. Li, T.-Y., Jeng, Y.-J., Chang, S.-I.: Simulating Virtual Human Crowds with a Leader-Follower Model. In: Proceedings of the Fourteenth Conference on Computer Animation, pp. 93–102 (2001)

    Google Scholar 

  25. Brackstone, M., McDonald, M.: Car-Following: A Historical Review. Transport. Res. F. 2(4), 181–196 (1999)

    Article  Google Scholar 

  26. Pipes, L.A.: An Operational Analysis of Traffic Dynamics. J. Appl. Phys. 24(3), 274 (1953)

    Article  MathSciNet  Google Scholar 

  27. Herman, R., Montroll, E.W., Potts, R.B., Rothery, R.W.: Traffic Dynamics: Analysis of Stability in Car-Following. Oper. Res. 7(1), 86–106 (1959)

    Article  MathSciNet  Google Scholar 

  28. Gazis, D.C., Herman, R., Rothery, R.W.: Nonlinear Follow-The-Leader Models of Traffic Flow. Oper. Res. 9(4), 545–567 (1961)

    Article  MATH  MathSciNet  Google Scholar 

  29. Helly, W.: Simulation of Bottlenecks in Single Lane Traffic Flow. In: Proceedings of the Symposium on Theory of Traffic Flow, pp. 207–238. Elsevier, New York (1959)

    Google Scholar 

  30. Fletcher, R.: Practical Methods of Optimization. Wiley, Hoboken (2000)

    Book  Google Scholar 

  31. Regan, D., Beverley, K.I.: Binocular and Monocular Stimuli for Motion in Depth: Changing-Disparity and Changing-Size Feed the Same Motion in Depth Stage. Vision Res. 19, 1331–1342 (1979)

    Article  Google Scholar 

  32. Heuer, H.: Estimates of Time-to-Contact Based on Changing Size and Changing Target Vergence. Perception 22(5), 549–563 (1993)

    Article  MathSciNet  Google Scholar 

  33. Gray, R., Regan, D.: Accuracy of Estimating Time To Collision Using Binocular and Monocular Information. Vision Res. 38, 499–512 (1997)

    Article  Google Scholar 

  34. Rushton, S.K., Wann, J.P.: Weighted Combination of Size and Disparity: A Computational Model for Timing a Ball Catch. Nat. Neurosci. 2(2), 186–190 (1999)

    Article  Google Scholar 

  35. Anderson, G.J., Sauer, C.W.: Optical Information for Car Following: The Driving by Visual Angle (DVA) Model. Hum. Factors 49(5), 878–896.

    Google Scholar 

  36. Tarr, M.J., Warren, W.H.: Virtual Reality in Behavioral Neuroscience and Beyond. Nat. Neurosci. 5, 1089–1092 (2002)

    Article  Google Scholar 

  37. Bülthoff, H.H., Mallot, H.A.: Integration of Depth Modules: Stereo and Shading. J. Opt. Soc. Am. A 5(10), 1749–1758 (1988)

    Article  Google Scholar 

  38. Costa, M.: Interpersonal Distances in Group Walking. J. Nonverbal Behav. 34(1), 15–26 (2010)

    Article  Google Scholar 

  39. Dyer, J.R.G., Johansson, A., Helbing, D., Couzin, I.D., Krause, J.: Leadership, Consensus Decision Making and Collective Behaviour in Humans. Phil. Trans. R. Soc. B. 364(1518), 781–789 (2009)

    Google Scholar 

  40. Dyer, J.R.G., Ioannou, C.C., Morrell, L.J., Croft, D.P., Couzin, I.D., Waters, D.A., Krause, J.: Consensus Decision Making in Human Crowds. Anim. Behav. 75(2), 461–470 (2008)

    Article  Google Scholar 

  41. Faria, J.J., Dyer, J.R.G., Tosh, C.R., Krause, J.: Leadership and Social Information Use in Human Crowds. Anim. Behav. 79(4), 895–901 (2010)

    Article  Google Scholar 

  42. Bonneaud, S., Rio, K., Chevaillier, P., Warren, W.H.: Accounting for Patterns of Collective Behavior in Crowd Locomotor Dynamics for Realistic Simulations. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds.) Transactions on Edutainment VIII, LNCS, vol. 7145, pp. 1–12. Springer, Heidelberg (2012)

    Google Scholar 

  43. Bonneaud, S., Warren, W.H.: A Behavioral Dynamics Approach to Modeling Realistic Pedestrian Behavior. In: Weidmann, U., Kirsch, U., Puffe, E., Weidmann, M. (eds.) Pedestrian and Evacuation Dynamics. Springer, Heidelberg (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA

    Kevin Rio & William H. Warren

Authors
  1. Kevin Rio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William H. Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kevin Rio .

Editor information

Editors and Affiliations

  1. und Transportsysteme (IVT), ETH Zürich Institut für Verkehrsplanung, Zürich, Switzerland

    Ulrich Weidmann

  2. Pestalozzi & Stäheli Ingenieurbüro Umwelt Mobilität Verkehr, Basel, Switzerland

    Uwe Kirsch

  3. und Verkehr, Universität Duisburg-Essen Physik von Transport, Duisburg, Germany

    Michael Schreckenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Rio, K., Warren, W.H. (2014). A Data-Driven Model of Pedestrian Following and Emergent Crowd Behavior. 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_47

Download citation

Publish with us

Policies and ethics

Search

Navigation