Using Raspberry Pi for Measuring Pedestrian Visiting Patterns via WiFi-Signals in Uncontrolled Field Studies

  • Peter M. KielarEmail author
  • Pavel Hrabák
  • Marek Bukáček
  • André Borrmann
Conference paper


Research on pedestrian behavior requires empirical field studies. A number of methods for data acquisition are available. However, a low-budget approach that can be applied to measure pedestrian destination choice in large-scale uncontrolled field studies is still missing. The measurement of destination choice patterns is important for validating strategic models, which describe in which order pedestrians visit locations to perform activities. We propose a Raspberry Pi setup for WiFi-based tracking of pedestrians by their handhelds in an anonymized manner. The method is useful for recording the microscopic and macroscopic crowd dynamics of large-scale uncontrolled field studies, e.g., public events. Furthermore, we provide a concept for strategic model validation that is based on the measurements.



We like to thank Antonin Danalet for discussions. Furthermore, the authors like to thank Daniel H. Biedermann and Micheal Rosteck for conducting the Christmas market field study. This work was partially supported by the Federal Ministry for Education and Research (BMBF) under the grant FKZ 13N12823, by the Czech Science Foundation under the grant GA15-15049S, and by Czech Technical University under the grant SGS15/214/ OHK4/3T/14.


  1. 1.
    Biedermann, D.H., Kielar, P.M., Riedl, A.M., Borrmann, A.: Oppilatio+ - a data and cognitive science based approach to analyze pedestrian flows in networks. Collective Dynamics 1(0), 1–30 (2016)CrossRefGoogle Scholar
  2. 2.
    Chattaraj, U., Seyfried, A., Chakroborty, P.: Comparison of pedestrian fundamental diagram across cultures. Adv. Complex Syst. 12(3), 393–405 (2009)CrossRefGoogle Scholar
  3. 3.
    Daamen, W., Yuan, Y., Duives, D., Hoogendoorn, S.P.: Comparing three types of real-time data collection techniques: counting cameras, Wi-Fi sensors and GPS trackers. In: Conference on Pedestrian and Evacuation Dynamics, pp. 568–574 (2016)Google Scholar
  4. 4.
    Danalet, A., Farooq, B., Bierlaire, M.: A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. Transp. Res. C Emerg. Technol. 44, 146–170 (2014)CrossRefGoogle Scholar
  5. 5.
    Danalet, A., Tinguely, L., de Lapparent, M., Bierlaire, M.: Location choice with longitudinal WiFi data. J. Choice Model. 18, 1–17 (2016)CrossRefGoogle Scholar
  6. 6.
    Dijkstra, J., Vries, B.D., Jessurun, J.: Wayfinding search strategies and matching familiarity in the built environment through virtual navigation. Transp. Res. Procedia 2, 141–148 (2014)CrossRefGoogle Scholar
  7. 7.
    Friis, H.T.: A note on a simple transmission formula. Proc. IRE 34(5), 254–256 (1946)CrossRefGoogle Scholar
  8. 8.
    Gärling, T.: Human information processing in sequential spatial choice. Wayfinding Behavior: Cognitive Mapping and other Spatial Processes pp. 81–98. Johns Hopkins University Press, Baltimore (1999)Google Scholar
  9. 9.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39(1), 1–24 (2005)CrossRefGoogle Scholar
  10. 10.
    Hoogendoorn, S.P., Bovy, P.H.L.: Pedestrian route-choice and activity scheduling theory and models. Transp. Res. B Methodol. 38(2), 169–190 (2004)CrossRefGoogle Scholar
  11. 11.
    Kielar, P.M., Borrmann, A.: Coupling spatial task solving models to simulate complex pedestrian behavior patterns. In: Conference on Pedestrian and Evacuation Dynamics (2016)Google Scholar
  12. 12.
    Kielar, P.M., Borrmann, A.: Modeling pedestrians’ interest in locations: a concept to improve simulations of pedestrian destination choice. Simul. Model. Pract. Theory 61, 47–62 (2016)CrossRefGoogle Scholar
  13. 13.
    Liu, C., Wu, K., He, T.: Sensor localization with ring overlapping based on comparison of received signal strength indicator. In: IEEE International Conference on Mobile Ad-hoc and Sensor Systems, 2004, pp. 516–518. IEEE, Piscataway (2004)Google Scholar
  14. 14.
    Rostek, M.: Evaluierung von messgeräten zur detektion von fußgängerströmen. Bachelor’s thesis, Technische Universität München (2017)Google Scholar
  15. 15.
    Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std. 802.11 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter M. Kielar
    • 1
    Email author
  • Pavel Hrabák
    • 2
  • Marek Bukáček
    • 2
  • André Borrmann
    • 1
  1. 1.Technische Universität MünchenMunichGermany
  2. 2.Faculty of Information TechnologyCzech Technical University in PraguePrague 6Czech Republic

Personalised recommendations