A Lightweight Approach for Building User Mobility Profiles

Abstract

Data captured by mobile devices enable us to learn, among other things, the places where users go, identify their home and workplace, the places they usually visit (e.g. supermarket, gym, etc.), the different paths they take to move from one place to another, and even their routines. In summary, with this information it is possible to learn a user mobility profile. In this work, we propose a lightweight approach for building mobility profiles from data collected with mobile devices. The mobility profiles of a user consists of the places visited, the visits history, and the travel paths. Our approach aims to solve some of the challenges and limitations identified in the literature. Particularly, it considers geographic information to identify certain kind of places such as open spaces, big places, and small places that are hard to distinguish with existing approaches. We use different sensors and time frequencies to collect data in order to optimize battery consumption and maximize precision. Finally, it executes entirely on the mobile devices, avoiding the exposure of sensitive user information and then preserving user privacy. The proposal was evaluated in the context of real usage of the developed prototype applications in two cities of Argentina. The results obtained with our approach outperformed other approaches of the literature, both in precision and recall.