Abstract
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.
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Acknowledgements
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.
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Kielar, P.M., Hrabák, P., Bukáček, M., Borrmann, A. (2019). Using Raspberry Pi for Measuring Pedestrian Visiting Patterns via WiFi-Signals in Uncontrolled Field Studies. In: Hamdar, S. (eds) Traffic and Granular Flow '17. TGF 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-11440-4_28
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DOI: https://doi.org/10.1007/978-3-030-11440-4_28
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