The Visual Computer

, Volume 29, Issue 12, pp 1277–1292 | Cite as

Right of way

Asymmetric agent interactions in crowds
  • Sean Curtis
  • Basim Zafar
  • Adnan Gutub
  • Dinesh Manocha
Original Article


Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf.


Pedestrian dynamics Crowd simulation Multiagent models Asymmetric responses 


  1. 1.
    Van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Inter. Symp. on Robotics Research (2009) Google Scholar
  2. 2.
    Blue, V.J., Adler, J.L.: Emergent fundamental pedestrian flows from cellular automata microsimulation. Transp. Res. Rec. 1644, 29–36 (1998) CrossRefGoogle Scholar
  3. 3.
    Blue, V.J., Adler, J.L.: Cellular automata microsimulation of bidirectional pedestrian flows. Transp. Res. Rec. 1678, 135–141 (1999) CrossRefGoogle Scholar
  4. 4.
    Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A 295, 507–525 (2001) CrossRefMATHGoogle Scholar
  5. 5.
    Chraibi, M., Seyfried, A., Schadschneider, A.: Generalized centrifugal-force model for pedestrian dynamics. Phys. Rev. E 82(4), 046,111 (2010) CrossRefGoogle Scholar
  6. 6.
    Curtis, S., Guy, S.J., Zafar, B., Manocha, D.: Virtual Tawaf: a case study in simulating the behavior of dense, heterogeneous crowds. In: 1st IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds (2011) Google Scholar
  7. 7.
    Durupinar, F., Pelechano, N., Allbeck, J., Gudukbay, U., Badler, N.: The impact of the ocean personality model on the perception of crowds. IEEE Comput. Graph. Appl. 31(3), 22–31 (2011) Google Scholar
  8. 8.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–762 (1998) CrossRefGoogle Scholar
  9. 9.
    Funge, J., Tu, X., Terzopoulos, D.: Cognitive modeling: knowledge, reasoning and planning for intelligent characters. In: SIGGRAPH, pp. 29–38. ACM, New York (1999) Google Scholar
  10. 10.
    Guy, S.J., Chhugani, J., Curtis, S., Lin, M.C., Dubey, P., Manocha, D.: Pledestrians: a least-effort approach to crowd simulation. In: Symposium on Computer Animation. ACM, New York (2010) Google Scholar
  11. 11.
    Guy, S.J., Kim, S., Lin, M.C., Manocha, D.: Simulating heterogeneous crowd behaviors using personality trait theory. In: SCA’11, pp. 43–52 (2011) Google Scholar
  12. 12.
    Guy, S.J., Lin, M.C., Manocha, D.: Modeling collision avoidance behavior for virtual humans. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 575–582 (2010) Google Scholar
  13. 13.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–790 (2000) CrossRefGoogle Scholar
  14. 14.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. 51(5), 4282–4286 (1995) CrossRefGoogle Scholar
  15. 15.
    Hirai, K., Tarui, K.: A simulation of the behavior of a crowd in panic. In: Proc. of the 1975 International Conference on Cybernetics and Society, pp. 409–411 (1975) Google Scholar
  16. 16.
    Johansson, A.D.H., Shukla, P.K.: Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Adv. Complex Syst. 10, 271–288 (2007) MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Ju, E., Choi, M.G., Park, M., Lee, J., Lee, K.H., Takahashi, S.: Morphable crowds. ACM Trans. Graph. 29(6), 140 (2010) CrossRefGoogle Scholar
  18. 18.
    Karamouzas, I., Heil, P., van Beek, P., Overmars, M.H.: A predictive collision avoidance model for pedestrian simulation. In: Motion in Games. Lecture Notes in Computer Science, vol. 5884, pp. 41–52. Springer, Berlin (2009) CrossRefGoogle Scholar
  19. 19.
    Kretz, T., Grünebohm, A., Schreckenberg, M.: Experimental study of pedestrian flow through a bottleneck. J. Stat. Mech. Theory Exp. P10014 (2006) Google Scholar
  20. 20.
    Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: Symposium on Computer Animation, pp. 109–118 (2007) Google Scholar
  21. 21.
    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), e10,047 (2010) CrossRefGoogle Scholar
  22. 22.
    Müller, K.: Zur Gestaltung und Bemessung von Fluchtwegen für die Evakuierung von Personen aus Bauwerken auf der Grundlage von Modellversuchen. Ph.D. thesis, Technische Hochschule Otto von Guericke, Magdeburg (1981) Google Scholar
  23. 23.
    Nagai, R., Fukamachi, M., Nagatan, T.: Evacuation of crawlers and walkers from corridor through an exit. Physica A 367, 449–460 (2006) CrossRefGoogle Scholar
  24. 24.
    Narain, R., Golas, A., Curtis, S., Lin, M.C.: Aggregate dynamics for dense crowd simulation. ACM Trans. Graph. 28, 122:1–122:8 (2009) CrossRefGoogle Scholar
  25. 25.
    Ondřej, J., Pettré, J., Olivier, A.H., Donikian, S.: A synthetic-vision based steering approach for crowd simulation. In: Proc. SIGGRAPH, pp. 123:1–123:9 (2010) Google Scholar
  26. 26.
    Patil, S., van den Berg, J., Curtis, S., Lin, M., Manocha, D.: Directing crowd simulations using navigation fields. In: IEEE TVCG, pp. 244–254 (2010) Google Scholar
  27. 27.
    Pelechano, N., Allbeck, J., Badler, N.: Controlling individual agents in high-density crowd simulation. In: SCA07 (2007) Google Scholar
  28. 28.
    Pettré, J., Ondřej, J., Olivier, A.H., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: SCA’09, pp. 189–198. ACM, New York (2009) Google Scholar
  29. 29.
    Pettré, J., Ondřej, J., Olivier, A.H., Cretual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA’09), pp. 189–198 (2009) CrossRefGoogle Scholar
  30. 30.
    Plaue, M., Chen, M., Bärwolff, G., Schwandt, H.: Trajectory extraction and density analysis of interesecting pedestrian flows from video recordings. In: Proceedings of the 2011 ISPRS Conference on Photogrammetric Image Analysis (2011) Google Scholar
  31. 31.
    Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. In: SIGGRAPH (1987) Google Scholar
  32. 32.
    Schadschneider, A.: Cellular automaton approach to pedestrian dynamics—theory. In: Pedestrian and Evacuation Dynamics, pp. 77–86 (2001) Google Scholar
  33. 33.
    Seyfried, A., Passon, O., Steffen, B., Boltes, M., Rupprecht, T., Klingsch, W.: New insights into pedestrian flow through bottlenecks. Transp. Sci. 43, 395–406 (2009) CrossRefGoogle Scholar
  34. 34.
    Seyfried, A., Schadschneider, A., Kemloh, U., Chraibi, M.: Force-based models of pedestrian dynamics. Netw. Heterog. Media 6(3), 425–442 (2011) MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. In: ACM SIGGRAPH 2006, pp. 1160–1168. ACM, New York (2006) CrossRefGoogle Scholar
  36. 36.
    Ulicny, B., Thalmann, D.: Towards interactive real-time crowd behavior simulation. Comput. Graph. Forum 21(4), 767–775 (2002) CrossRefGoogle Scholar
  37. 37.
    Yamamoto, K., Kokubo, S., Nishinari, K.: Simulation for pedestrian dynamics by real-coded cellular automata (RCA). Physica A 379, 654–660 (2007) CrossRefGoogle Scholar
  38. 38.
    Yeh, H., Curtis, S., Patil, S., van den Berg, J., Manocha, D., Lin, M.: Composite agents. In: Proc. of SCA, pp. 39–47 (2008) Google Scholar
  39. 39.
    Yu, Q., Terzopoulos, D.: A decision network framework for the behavioral animation of virtual humans. In: Symposium on Computer Animation, pp. 119–128 (2007) Google Scholar
  40. 40.
    Yu, W.J., Chen, R., Dong, L.Y., Dai, S.Q.: Centrifugal force model for pedestrian dynamics. Phys. Rev. E 72, 026,112 (2005) CrossRefGoogle Scholar
  41. 41.
    Zafar, B.: Analysis of the Mataf–Ramadan 1432 AH. Tech. rep, Hajj Research Institute, Umm al-Qura University, Saudi Arabia (2011) Google Scholar
  42. 42.
    Zanlungo, F., Ikeda, T., Kanda, T.: Social force model with explicit collision prediction. Europhys. Lett. 93, 68,005 (2011) CrossRefGoogle Scholar
  43. 43.
    Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Transitions in pedestrian fundamental diagrams of straight corridors and t-junctions. J. Stat. Mech. 2011(06), P06,004 (2011) CrossRefGoogle Scholar
  44. 44.
    Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram. J. Stat. Mech. 2012(02), P02,002 (2012) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sean Curtis
    • 1
  • Basim Zafar
    • 2
  • Adnan Gutub
    • 3
  • Dinesh Manocha
    • 1
  1. 1.University of North Carolina at Chapel HillChapel HillUSA
  2. 2.Hajj Research InstituteUmm Al-Qura UniversityMeccaSaudi Arabia
  3. 3.Center of Research Excellence in Hajj and Omrah (HajjCoRE)Umm al-Qura UniversityMeccaSaudi Arabia

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