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Modeling a Crowd of Groups: Multidisciplinary and Methodological Challenges

  • Stefania Bandini
  • Giuseppe Vizzari
Chapter
Part of the The International Series in Video Computing book series (VICO, volume 11)

Abstract

The main aim of the chapter is to introduce a recent and current trend of research in the modeling, simulation and visual analysis of crowds: the study of the impact of groups on the overall crowd dynamics, and its implications of the aforementioned research activities as well as their outcomes. In most situations, in fact, a crowd of pedestrians is more than a simple set of individuals, each interpreting the presence of the others in a uniform way, trying to preserve a certain distance from the nearest person. A crowd is rather a composite assembly of individuals, some of which are bound by different types of ties, not only representing the presence of other pedestrians as a repulsive force, influencing their attitude towards the movement in the environment. Current models for the simulation of crowds of pedestrians have just started to analyze this phenomenon, and we still lack a complete understanding of the implications of not considering it, either in a real simulation project supporting decision making activities of designers or planners, or in the analysis and automatic extraction of information, for instance from video footage of events or crowded environments.

Keywords

Cellular Automaton Total Travel Time Space Utilization Crowd Behavior Fundamental Diagram 
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.

Notes

Acknowledgements

This work is a result of the Crystal Project, funded by the Center of Research Excellence in Hajj and Omrah (Hajjcore), Umm Al-Qura University, Makkah, Saudi Arabia. Our acknowledgement for the common work in the project and for fruitful discussions goes to Katsuhiro Nishinari (RCAST – Research Center for Advanced Science and Technology, The University of Tokyo, Japan), our valuable partner within the Crystals Project. We also thank Ugo Fabietti (CREAM – University of Milano-Bicocca) for his contribution from the area of Anthropology.

References

  1. 1.
    Bandini, S., Federici, M.L., Vizzari, G.: Situated cellular agents approach to crowd modeling and simulation. Cybern. Syst. 38(7), 729–753 (2007)CrossRefMATHGoogle Scholar
  2. 2.
    Bandini, S., Manenti, L., Manzoni, S., Sartori, F.: A knowledge-based approach to crowd classification. In: Proceedings of the 5th International Conference on Pedestrian and Evacuation Dynamics, March 8–10, Gaithersburg, MD, USA (2010)Google Scholar
  3. 3.
    Bandini, S., Manzoni, S., Redaelli, S.: Towards an ontology for crowds description: a proposal based on description logic. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds.) ACRI. Lecture Notes in Computer Science, vol. 5191, pp. 538–541. Springer, Berlin, Germany (2008)Google Scholar
  4. 4.
    Bandini, S., Manzoni, S., Vizzari, G.: Agent based modeling and simulation: an informatics perspective. J. Artif. Soc. Soc. Simul. 12(4), 4 (2009)Google Scholar
  5. 5.
    Bandini, S., Rubagotti, F., Vizzari, G., Shimura, K.: An agent model of pedestrian and group dynamics: experiments on group cohesion. In: Pirrone, R., Sorbello, F. (eds.) AI*IA. Lecture Notes in Computer Science, vol. 6934, pp. 104–116. Springer, Berlin, Germany (2011)Google Scholar
  6. 6.
    Batty, M.: Agent based pedestrian modeling (editorial). Environ. Plan. B: Plan. Des. 28, 321–326 (2001)CrossRefGoogle Scholar
  7. 7.
    Blue, V.J., Adler, J.L.: Cellular automata microsimulation of bi-directional pedestrian flows. Transp. Res. Rec. 1678, 135–141 (1999)CrossRefGoogle Scholar
  8. 8.
    Blue, V.J., Adler, J.L.: Modeling four-directional pedestrian flows. Trans. Res. Rec. 1710, 20–27 (2000)CrossRefGoogle Scholar
  9. 9.
    Bonomi, A., Manenti, L., Manzoni, S., Vizzari, G.: Makksim: dealing with pedestrian groups in MAS-based crowd simulation. In: Fortino, G., Garro, A., Palopoli, L., Russo, W., Spezzano, G. (eds.) WOA. CEUR Workshop Proceedings, Rende, vol. 741, pp. 166–170 (2011). http://CEUR-WS.org
  10. 10.
    Canetti, E.: Crowds and power. Farrar, Straus and Giroux, New York (1984)Google Scholar
  11. 11.
    Challenger, R., Clegg, C.W., Robinson, M.A.: Understanding crowd behaviours: Supporting evidence. Tech. rep., University of Leeds (2009)Google Scholar
  12. 12.
    Chattaraj, U., Seyfried, A., Chakroborty, P.: Comparison of pedestrian fundamental diagram across cultures. Adv. Complex Syst. 12(3), 393–405 (2009)CrossRefGoogle Scholar
  13. 13.
    Costa, M.: Interpersonal distances in group walking. J. Nonverbal Behav. 34, 15–26 (2010). http://dx.doi.org/10.1007/s10919-009-0077-y, doi:10.1007/s10919-009-0077-yGoogle Scholar
  14. 14.
    Dijkstra, J., Jessurun, J., de Vries, B., Timmermans, H.J.P.: Agent architecture for simulating pedestrians in the built environment. In: International Workshop on Agents in Traffic and Transportation, pp. 8–15, Hakodate, Japan (2006)Google Scholar
  15. 15.
    Dopfer, K., Foster, J., Potts, J.: Micro-meso-macro. J. Evol. Econ. 14, 263–279 (2004). http://dx.doi.org/10.1007/s00191-004-0193-0, doi:10.1007/s00191-004-0193-0Google Scholar
  16. 16.
    Fabietti, U.E.M.: Gruppi – Antropologia, vol. Enciclopedia delle Scienze Sociali, pp. 424–429. Treccani (1994)Google Scholar
  17. 17.
    Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G.: An innovative scenario for pedestrian data collection: the observation of an admission test at the university of Milano-Bicocca. In: Proceedings of the 6th International Conference on Pedestrian and Evacuation Dynamics – PED 2012, Zurich, Switzerland (2012)Google Scholar
  18. 18.
    Fruin, J.J.: Pedestrian planning and design. Metropolitan Association of Urban Designers and Environmental Planners, New York (1971)Google Scholar
  19. 19.
    Georgoudas, I.G., Sirakoulis, G.C., Andreadis, I.: An anticipative crowd management system preventing clogging in exits during pedestrian evacuation processes. IEEE Syst. J. 5(1), 129–141 (2011)CrossRefGoogle Scholar
  20. 20.
    Gloor, C., Stucki, P., Nagel, K.: Hybrid techniques for pedestrian simulations. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) 6th International Conference on Cellular Automata for Research and Industry, ACRI 2004. Lecture Notes in Computer Science, vol. 3305, pp. 581–590. Springer, Berlin, Germany (2004)Google Scholar
  21. 21.
    Gualdi, G., Prati, A., Cucchiara, R.: Contextual information and covariance descriptors for people surveillance: An application for safety of construction workers. EURASIP J. Image Video Process. 2011 (2011)Google Scholar
  22. 22.
    Hall, E.T.: A system for the notation of proxemic behavior. Am. Anthropol. 65(5), 1003–1026 (1963). http://www.jstor.org/stable/668580
  23. 23.
    Hall, E.T.: The Hidden Dimension. Anchor Books, New York (1966)Google Scholar
  24. 24.
    Helbing, D.: A fluid–dynamic model for the movement of pedestrians. Complex Syst. 6(5), 391–415 (1992)MathSciNetMATHGoogle Scholar
  25. 25.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)CrossRefGoogle Scholar
  26. 26.
    Helbing, D., Schweitzer, F., Keltsch, J., Molnár, P.: Active walker model for the formation of human and animal trail systems. Phys. Rev. E 56(3), 2527–2539 (1997)CrossRefGoogle Scholar
  27. 27.
    Henein, C.M., White, T.: Agent-based modelling of forces in crowds. In: Davidsson, P., Logan, B., Takadama, K. (eds.) Joint Workshop on Multi-agent and Multi-agent-based Simulation, MABS 2004, New York, 19 July 2004, Revised Selected Papers. Lecture Notes in Computer Science, vol. 3415, pp. 173–184. Springer (2005)Google Scholar
  28. 28.
    Junior, J.C.J., Musse, S.R., Jung, C.R.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010)Google Scholar
  29. 29.
    Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Phys. A: Stat. Mech. Appl. 312(1–2), 260–276 (2002). http://www.sciencedirect.com/science/article/pii/S0378437102008579
  30. 30.
    Klüpfel, H.: A cellular automaton model for crowd movement and egress simulation. P.hd. thesis, University Duisburg-Essen (2003)Google Scholar
  31. 31.
    Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.: Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker. In: ICCV Workshops, pp. 120–127. IEEE, Barcelona, Spain (2011)Google Scholar
  32. 32.
    Manenti, L., Manzoni, S., Vizzari, G., Ohtsuka, K., Shimura, K.: Towards an agent-based proxemic model for pedestrian and group dynamic. In: Omicini, A., Viroli, M. (eds.) WOA. CEUR Workshop Proceedings, vol. 621, Rimini, Italy (2010). http://CEUR-WS.org
  33. 33.
    Manenti, L., Manzoni, S., Vizzari, G., Ohtsuka, K., Shimura, K.: An agent-based proxemic model for pedestrian and group dynamics: motivations and first experiments. In: Villatoro, D., Sabater-Mir, J., Sichman, J.S. (eds.) MABS. Lecture Notes in Computer Science, vol. 7124, pp. 74–89. Springer Berlin, Germany (2011)Google Scholar
  34. 34.
    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). http://dx.doi.org/10.1371%2Fjournal.pone.0010047
  35. 35.
    Musse, S.R., Thalmann, D.: Hierarchical model for real time simulation of virtual human crowds. IEEE Trans. Vis. Comput. Graph. 7(2), 152–164 (2001)CrossRefGoogle Scholar
  36. 36.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. Journal de Physique I France 2(2221), 222–235 (1992)Google Scholar
  37. 37.
    Nishinari, K., Kirchner, A., Namazi, A., Schadschneider, A.: Extended floor field ca model for evacuation dynamics. IEICE Trans. Inf. syst. 87(3), 726–732 (2004)Google Scholar
  38. 38.
    Nishinari, K., Suma, Y., Yanagisawa, D., Tomoeda, A., Kimura, A., Nishi, R.: Toward smooth movement of crowds. In: Pedestrian and Evacuation Dynamics 2008, pp. 293–308. Springer, Berlin/Heidelberg (2008)Google Scholar
  39. 39.
    Okazaki, S.: A study of pedestrian movement in architectural space, part 1: pedestrian movement by the application of magnetic models. Trans. A.I.J. 283, 111–119 (1979)Google Scholar
  40. 40.
    Paris, S., Donikian, S.: Activity-driven populace: A cognitive approach to crowd simulation. IEEE Comput. Graph. Appl. 29(4), 34–43 (2009)CrossRefGoogle Scholar
  41. 41.
    Patil, S., van den Berg, J.P., Curtis, S., Lin, M.C., Manocha, D.: Directing crowd simulations using navigation fields. IEEE Trans. Vis. Comput. Graph. 17(2), 244–254 (2011)Google Scholar
  42. 42.
    Qiu, F., Hu, X.: Modeling group structures in pedestrian crowd simulation. Simul. Model. Pract. Theory 18(2), 190–205 (2010)CrossRefGoogle Scholar
  43. 43.
    Raghavendra, R., Bue, A.D., Cristani, M., Murino, V.: Abnormal crowd behavior detection by social force optimization. In: Salah, A.A., Lepri, B. (eds.) HBU. Lecture Notes in Computer Science, vol. 7065, pp. 134–145. Springer, Berlin, Germany (2011)Google Scholar
  44. 44.
    Sarmady, S., Haron, F., Talib, A.Z.H.: Modeling groups of pedestrians in least effort crowd movements using cellular automata. In: Al-Dabass, D., Triweko, R., Susanto, S., Abraham, A. (eds.) Asia International Conference on Modelling and Simulation, pp. 520–525. IEEE Computer Society, Bali, Indonesia (2009)Google Scholar
  45. 45.
    Schadschneider, A., Kirchner, A., Nishinari, K.: CA approach to collective phenomena in pedestrian dynamics. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) 5th International Conference on Cellular Automata for Research and Industry, ACRI 2002. Lecture Notes in Computer Science, vol. 2493, pp. 239–248. Springer, Berlin, Germany (2002)Google Scholar
  46. 46.
    Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation dynamics: empirical results, modeling and applications. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 3142–3176. Springer, New York (2009)CrossRefGoogle Scholar
  47. 47.
    Schreckenberg, M., Sharma, S.D. (eds.): Pedestrian and Evacuation Dynamics. Springer, Berlin, Germany (2001)Google Scholar
  48. 48.
    Schultz, M., Schulz, C., Fricke, H.: Passenger dynamics at airport terminal environment. In: Klingsch, W.W.F., Rogsch, C., Schadschneider, A., Schreckenberg, M. (eds.) Pedestrian and Evacuation Dynamics 2008, pp. 381–396. Springer, Heidelberg/New York (2010)CrossRefGoogle Scholar
  49. 49.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graph. Models 69(5–6), 246–274 (2007)CrossRefGoogle Scholar
  50. 50.
    Toyama, M.C., Bazzan, A.L.C., da Silva, R.: An agent-based simulation of pedestrian dynamics: from lane formation to auditorium evacuation. In: Nakashima, H., Wellman, M.P., Weiss, G., Stone, P. (eds.) 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp. 108–110. ACM, Hakodate, Japan (2006)Google Scholar
  51. 51.
    Was, J.: Crowd dynamics modeling in the light of proxemic theories. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC (2). Lecture Notes in Computer Science, vol. 6114, pp. 683–688. Springer, Berlin, Germany (2010)Google Scholar
  52. 52.
    Willis, A., Gjersoe, N., Havard, C., Kerridge, J., Kukla, R.: Human movement behaviour in urban spaces: implications for the design and modelling of effective pedestrian environments. Environ. Plan. B 31(6), 805–828 (2004)CrossRefGoogle Scholar
  53. 53.
    Xu, S., Duh, H.B.L.: A simulation of bonding effects and their impacts on pedestrian dynamics. IEEE Trans. Intell. Transp. Syst. 11(1), 153–161 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Science, Systems and Communication, Complex Systems and Artificial Intelligence (CSAI) Research CenterUniversity of Milan – BicoccaMilanoItaly

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