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

Peter M. Kielar (), Pavel Hrabák (), Marek Bukáček () and André Borrmann ()
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Peter M. Kielar: Technische Universität München
Pavel Hrabák: Czech Technical University in Prague, Faculty of Information Technology
Marek Bukáček: Czech Technical University in Prague, Faculty of Information Technology
André Borrmann: Technische Universität München

A chapter in Traffic and Granular Flow '17, 2019, pp 245-253 from Springer

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-11440-4_28

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DOI: 10.1007/978-3-030-11440-4_28

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