Identifying Stops and Moves in WiFi Tracking Data

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 11; p. 4039
• Main Authors: Chilipirea, Cristian, Baratchi, Mitra, Dobre, Ciprian, van Steen, Maarten
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 19.11.2018; MDPI AG
• Subjects: crowd movement analysis; mobility modeling; trajectory data mining; WiFi tracking; crowd movement analysis; mobility modeling; trajectory data mining; WiFi tracking
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18114039

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.