Modeling mobility patterns

Abstract

This work focuses on analysis and model generation for user mobility patterns given a sequence of observed WiFi signals. Built on the Android platform, the data collection mobile application gathers WiFi sensor readings (BSSID and SSID). The implemented pipeline performs location identification using an online hierarchical timeline clustering algorithm and segmentation algorithm. The segmentation algorithm constructs a tree of location candidates which are then aggregated by a similarity measure based on their BSSID and SSID features. The generated locations are processed to extract mobility patterns. A pattern is a sequence of location transitions which have high information content, high activity over time, and high degree of predictability. Each of these aspects are described by a numerical measure based on statistical properties of the location observations in a feature space.