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Open AccessArticle

Identifying Stops and Moves in WiFi Tracking Data

1
University Politehnica of Bucharest, Romania, Computer Science Department, Splaiul Independenței 313, 060042 Bucharest, Romania
2
Leiden University, Rapenburg 70, 2311 Leiden, The Netherlands
3
University of Twente, 7522 Enschede, The Netherlands
4
ICI Bucharest, Bulevardul Mareșal Alexandru Averescu, 011454 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(11), 4039; https://doi.org/10.3390/s18114039
Received: 30 September 2018 / Revised: 8 November 2018 / Accepted: 15 November 2018 / Published: 19 November 2018
There are multiple methods for tracking individuals, but the classical ones such as using GPS or video surveillance systems do not scale or have large costs. The need for large-scale tracking, for thousands or even millions of individuals, over large areas such as cities, requires the use of alternative techniques. WiFi tracking is a scalable solution that has gained attention recently. This method permits unobtrusive tracking of large crowds, at a reduced cost. However, extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the dependence on signals generated by the tracked device, which are irregular and sparse. To facilitate further data analysis, we can partition individual trajectories into periods of stops and moves. This abstraction level is fundamental, and it opens the way for answering complex questions about visited locations or even social behavior. Determining stops and movements has been previously addressed for tracking data gathered using GPS. GPS trajectories have higher positional accuracy at a fixed, higher frequency as compared to trajectories obtained through WiFi. However, even with the increase in accuracy, the problem, of separating traces in periods of stops and movements, remains similar to the one we encountered for WiFi tracking. In this paper, we study three algorithms for determining stops and movements for GPS-based datasets and explore their applicability to WiFi-based data. We propose possible improvements to the best-performing algorithm considering the specifics of WiFi tracking data. View Full-Text
Keywords: crowd movement analysis; trajectory data mining; WiFi tracking; mobility modeling crowd movement analysis; trajectory data mining; WiFi tracking; mobility modeling
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MDPI and ACS Style

Chilipirea, C.; Baratchi, M.; Dobre, C.; Steen, M. Identifying Stops and Moves in WiFi Tracking Data. Sensors 2018, 18, 4039.

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