Advertisement

Evaluation of Pedestrian Density Distribution with Respect to the Velocity Response

  • Marek BukáčekEmail author
  • Jana Vacková
Conference paper

Abstract

There are many approaches to evaluate density within pedestrian scenarios including point approximation, Voronoi cells or more sophisticated methods. In this project we focus on the individual density, where each pedestrian is considered as a source of density distribution. A cone can be used as a reasonable shape with its diameter as a blur parameter. Naturally, pedestrians adapt their velocity and path selection with respect to the conditions around them in given range. The correlation of density and velocity was evaluated on laboratory experiment data for all acceptable blur–range combinations. Because of the fact that negative correlation corresponds to a more significant response of velocity to the density, the correlations seem to be a perfect tool to estimate density parameters. Surprisingly, the expected negative correlation was observed only for one segment of pedestrian’s trajectory, observations were much more complex.

Notes

Acknowledgements

This work was supported by the Czech Science Foundation under the grant GA15-15049S and by Czech Technical University under the grant SGS15/214/OHK4/3T/14. We would like to thank GAMS team members, namely Pavel Hrabák and Matěj Kotrba, for significant help with organizing the experiment. All experiment participants approve for the experimental data to be stored, used and published for academic purposes.

References

  1. 1.
    Bode, N.W.F., Codling, E.A.: Statistical models for pedestrian behaviour in front of bottlenecks. In: TGF’15, pp. 81–88. Springer, Cham (2016)CrossRefGoogle Scholar
  2. 2.
    Bukáček, M., Hrabák, P., Krbálek, M.: Experimental study of phase transition in pedestrian flow. Transp. Res. Procedia 2, 105–113 (2014). In: PED’14CrossRefGoogle Scholar
  3. 3.
    Bukáček, M., Hrabák, P., Krbálek, M.: Individual microscopic results of bottleneck experiments. In: TGF’15, pp. 105–112. Springer, Cham (2016)CrossRefGoogle Scholar
  4. 4.
    Bukáček, M., Hrabák, P., Krbálek, M.: Microscopic travel time analysis of bottleneck experiments. Transportmetrica A: Transp. Sci. 14, 1–20 (2018)Google Scholar
  5. 5.
    Campanella, M., Hoogendoorn, S.P., Daamen, W.: Effects of heterogeneity on self-organized pedestrian flows. Transp. Res. Rec. 2124, 148–156 (2009)CrossRefGoogle Scholar
  6. 6.
    Duives, D., Winnie, D., Hoogendoorn, S.P.: Anticipation behavior upstream of a bottleneck. Transp. Res. Procedia 2, 43–50 (2014). In: PED’14CrossRefGoogle Scholar
  7. 7.
    Kretz, T., Grunebohm, A., Schreckenberg, M.: Experimental study of pedestrian flow through a bottleneck. J. Stat. Mech: Theory Exp. 10, 1–20 (2006)zbMATHGoogle Scholar
  8. 8.
    Liao, W., Tordeux, A., Seyfried, A., et al.: Measuring the steady state of pedestrian flow in bottleneck experiments. Physica A 461, 248–261 (2016)CrossRefGoogle Scholar
  9. 9.
    Schadschneider, A., Chowdhury, D., Nishinari, K.: Stochastic Transport in Complex Systems. Elsevier, Amsterdam (2010)zbMATHGoogle Scholar
  10. 10.
    Seyfried, A., Passon, O., Steffen, B., et al.: New insights into pedestrian flow through bottlenecks. Transp. Sci. 43(3), 395–406 (2009)CrossRefGoogle Scholar
  11. 11.
    Steffen, B., Seyfried, A.: Methods for measuring pedestrian density, flow, speed and direction with minimal scatter. Physica A 389(9), 1902–1910 (2010)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Seyfried, A.: Quantification of bottleneck effects for different types of facilities. Transp. Res. Procedia 2, 51–59 (2014). In PED’14CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Czech Technical UniversityPragueCzech Republic

Personalised recommendations