Fuzzy Prediction of Pedestrian Steering Behavior with Local Environmental Effects

  • Mojdeh Nasir
  • Matthew Glenn Watson
  • Vu Le
  • Saeid Nahavandi
  • Douglas Creighton
Conference paper

Abstract

This research focuses on prediction of pedestrian walking paths in indoor public environments during normal and non-panic situations. The aim is to incorporate uncertain and non-precise aspects of pedestrian interaction with the environment to enhance steering behavior modeling. The proposed model introduces a fuzzy logic framework to predict the impact of environmental stimuli within a pedestrian’s field of view on movement direction. The environment is treated as a set of discrete attractions and repulsions. Attractive and repulsive effects of the surrounding environment, which drive the pedestrian to select next step position, are quantified by social force method. A high flow corridor in an office is considered for the case study. Stochastic simulation is used to generate walking trajectories and calculate a dynamic contour map of environmental stimuli in each step. To verify the simulation results and gain a better insight into the problem, a dataset defining walking trajectories of 25 participants passing through that hallway was collected using motion tracking system. Results demonstrate a strong correlation between real data and simulated results.

Keywords

Pedestrian steering behavior Fuzzy logic Environmental effects Walking trajectory prediction 

References

  1. 1.
    Lynch, K.: The image of the city. Cambridge Technology Press, MA, USA (1960) Google Scholar
  2. 2.
    Raubal, M., Worboys, M.: A formal model of the process of wayfinding in built environments. In: Spatial information theory. Cognitive and computational foundations of geographic information science . Freksa, C., Mark, D. M. (eds.) LNCS, vol. 1661, pp. 381–399. Springer, Heidelberg (1999) Google Scholar
  3. 3.
    Golledge, R. G., Ruggles, A. J., Pellegrino, J. W., Gale, N. D.: Integrating route knowledge in an unfamiliar neighborhood: Along and across route experiments. Journal of Environmental Psychology, 13(4), 293–307 (1993) Google Scholar
  4. 4.
    Wineman, J. D., Peponis, J.: Constructing spatial meaning: Spatial affordances in museum design. Environment and Behavior, 42(1), 86–109 (2010) Google Scholar
  5. 5.
    Fajen, B. R., Warren, W. H.: Behavioral dynamics of steering, obstable avoidance, and route selection. Journal of Experimental Psychology: Human Perception and Performance, 29(2), 343–362 (2003) Google Scholar
  6. 6.
    Li, C.: User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding. Computers, Environment and Urban Systems, 30(6), 726–740 (2006) Google Scholar
  7. 7.
    Ridwan, M.: Fuzzy preference based traffic assignment problem. Transportation Research Part C: Emerging Technologies, 12(3–4), 209–233 (2004) Google Scholar
  8. 8.
    Seraji, H., Howard, A.: Behavior-based robot navigation on challenging terrain: A fuzzy logic approach. IEEE Transactions on Robotics and Automation,  18(3), 308–321 (2002) Google Scholar
  9. 9.
    Liu, X., Karimi, H. A.: Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30(6), 741–756 (2006) Google Scholar
  10. 10.
    Zacharias, J., Stathopoulos, T., Wu, H.: Spatial behavior in san francisco’s plazas. Environment and Behavior, 36(5), 638–658 (2004) Google Scholar
  11. 11.
    Bierlaire, M., Antonini, G., Weber, M.: Behavioral dynamics for pedestrians. In: 10th International Conference on Travel Behavior Research , pp. 1–22. Lucerne (2003) Google Scholar
  12. 12.
    Creem-Regehr, S. H., Willemsen, P., Gooch, A. A., Thompson, W. B.: The influence of restricted viewing conditions on egocentric distance perception: Implications for real and virtual indoor environments. Perception, 34(2), 191–204 (2005) Google Scholar
  13. 13.
    AlGadhi, S. A. H., Mahmassani, H. S.: Simulation of crowd behavior and movement: Fundamental relations and application. Transportation Research Record, 1320(1320), 260–268 (1991) Google Scholar
  14. 14.
    Robin, T., Antonini, G., Bierlaire, M., Cruz, J.: Specification, estimation and validation of a pedestrian walking behavior model. Transportation Research Part B-Methodological, 43(1), 36–56 (2009) Google Scholar
  15. 15.
    Golledge, R. G.: Human wayfinding and cognitive maps. In: Wayfinding behavior, cognitive mapping and other spatial processes . Golledge, R. (ed.), pp. 5–45. The Johns Hopkins University Press, Baltimore and London (1999) Google Scholar
  16. 16.
    Helbing, D.: A mathematical model for the behavior of pedestrians. Behavioral Science, 36(4), 298–310 (1991) Google Scholar
  17. 17.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature, 407(6803), 487–490 (2000) Google Scholar
  18. 18.
    Helbing, D., Molnar, P., Farkas, I. J., Bolay, K.: Self-organizing pedestrian movement. Environment and Planning B: Planning and Design, 28(3), 361–383 (2001) Google Scholar
  19. 19.
    Warren, W. H., Fajen, B. R.: Behavioral dynamics of human locomotion. Ecological Psychology, 16(1), 61–66 (2004) Google Scholar
  20. 20.
    Weidmann, U.: Transporttechnik der fussgaenger. Series Schriftenreihe des Instituts fur Verkehrsplanung, Strassen-und Eisenbahnbau, Technical Report 90, ETH Zurich, Switzerland (1993) Google Scholar
  21. 21.
    Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian walking behavior. Transportation Research Part B: Methodological,  40(8), 667–687 (2006) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mojdeh Nasir
    • 1
  • Matthew Glenn Watson
    • 1
  • Vu Le
    • 1
  • Saeid Nahavandi
    • 1
  • Douglas Creighton
    • 1
  1. 1.Centre for Intelligent Systems ResearchDeakin UniversityVictoriaAustralia

Personalised recommendations