Asymmetric Pedestrian Dynamics on a Staircase Landing from Continuous Measurements

  • Alessandro CorbettaEmail author
  • Chung-Min Lee
  • Adrian Muntean
  • Federico Toschi
Conference paper


We investigate via extensive experimental data the dynamics of pedestrians walking in a corridor-shaped landing in a building at Eindhoven University of Technology. With year-long automatic measurements employing a Microsoft Kinect™ 3D-range sensor and ad hoc tracking techniques, we acquired few hundreds of thousands pedestrian trajectories in real-life conditions. Here, we discuss the asymmetric features of the dynamics in the two walking directions with respect to the flights of stairs (i.e. ascending or descending). We provide a detailed analysis of position and speed fields for the cases of pedestrians walking alone undisturbed and for couple of pedestrians in counter-flow. Then, we show average walking velocities exploring all the observed combinations in terms of numbers of pedestrians and walking directions.


Particle Tracking Velocimetry Head Tracking Walking Direction Pedestrian Traffic Speed Drop 
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.



We thank A. Holten and G. Oerlemans (Eindhoven, NL) for their help in the establishment of the measurement set-up at Eindhoven University of Technology and A. Liberzon (Tel Aviv, IL) for his help in the adaptation of the OpenPTV library. We acknowledge Iker Zuriguel (Pamplona, Spain) for the discussions during the TGF ’15 conference that led to Fig. 4. We acknowledge the support from the Brilliant Streets research program of the Intelligent Lighting Institute at the Eindhoven University of Technology, NL. AC was founded by a Lagrange Ph.D. scholarship granted by the CRT Foundation, Turin, IT and by Eindhoven University of Technology, NL.


  1. 1.
    Boltes, M., Seyfried, A.: Collecting pedestrian trajectories. Neurocomputing 100, 127–133 (2013)CrossRefGoogle Scholar
  2. 2.
    Brščić, D., Kanda, T., Ikeda, T., Miyashita, T.: Person tracking in large public spaces using 3-D range sensors. IEEE Trans. Hum. Mach. Syst. 43(6), 522–534 (2013)CrossRefGoogle Scholar
  3. 3.
    Brščić, D., Zanlungo, F., Kanda, T.: Density and velocity patterns during one year of pedestrian tracking. Transp. Res. Procedia 2, 77–86 (2014)CrossRefGoogle Scholar
  4. 4.
    Corbetta, A., Bruno, L., Muntean, A., Toschi, F.: High statistics measurements of pedestrian dynamics. Transp. Res. Procedia 2, 96–104 (2014)CrossRefGoogle Scholar
  5. 5.
    Corbetta, A., Lee, C., Benzi, R., Muntean, A., Toschi, F.: Fluctuations and mean behaviours in diluted pedestrian flows (2015). ManuscriptGoogle Scholar
  6. 6.
    Corbetta, A., Muntean, A., Vafayi, K.: Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method. Math. Biosci. Eng. MBE 12(2), 337–356 (2015)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Hoskins, B.L., Milke, J.A.: Differences in measurement methods for travel distance and area for estimates of occupant speed on stairs. Fire Saf. J. 48, 49–57 (2012)CrossRefGoogle Scholar
  8. 8.
    Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., Theraulaz, G.: Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc. Roy. Soc. Lond. B Biol. Sci. 276, 2755–2762 (2009)CrossRefGoogle Scholar
  9. 9.
    Roggen, D., Wirz, M., Tröster, G., Helbing, D.: Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods. Netw. Heterogen. Media 6(3), 521–544 (2011)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Ronchi, E., Reneke, P.A., Kuligowski, E.D., Peacock, R.D.: An analysis of evacuation travel paths on stair landings by means of conditional probabilities. Fire Saf. J. 65, 30–40 (2014)CrossRefGoogle Scholar
  11. 11.
    Seer, S., Brändle, N., Ratti, C.: Kinects and human kinetics: a new approach for studying pedestrian behavior. Transp. Res. Part C Emerg. Technol. 48, 212–228 (2014)CrossRefGoogle Scholar
  12. 12.
    The OpenPTV Consortium: OpenPTV: Open source particle tracking velocimetry. (2012)
  13. 13.
    Willneff, J.: A spatio-temporal matching algorithm for 3D particle tracking velocimetry. Ph.D. thesis, ETH Zürich (2003)Google Scholar
  14. 14.
    Zanlungo, F., Ikeda, T., Kanda, T.: Potential for the dynamics of pedestrians in a socially interacting group. Phys. Rev. E 89, 012811 (2014)Google Scholar
  15. 15.
    Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A.: Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions. J. Stat. Mech. Theory Exp. 2011(6), P06004 (2011)Google Scholar
  16. 16.
    Zhang, J., Seyfried, A.: Comparison of intersecting pedestrian flows based on experiments. Phys. A 405, 316–325 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alessandro Corbetta
    • 1
    • 2
    Email author
  • Chung-Min Lee
    • 3
  • Adrian Muntean
    • 4
  • Federico Toschi
    • 1
    • 5
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Politecnico di TorinoTurinItaly
  3. 3.California State University Long BeachLong BeachUSA
  4. 4.Karlstad UniversityKarlstadSweden
  5. 5.CNR-IACRomeItaly

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