WiFi Fingerprint Clustering for Urban Mobility Analysis
This addresses the problem of urban mobility analysis for researchers and practitioners by providing a method to overcome GPS limitations in indoor and dense areas, though it appears incremental as it builds on existing WiFi-based techniques.
The paper tackles the problem of identifying user points of interest and mobility patterns in urban environments where GPS is inaccurate, by using WiFi fingerprint clustering. The result shows that combining WiFi with GPS enables the identification of indoor POI, neighborhood activity, and micro-mobility, which GPS alone cannot achieve.
In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.