Adaptive Learning of Semantic Locations and Routes

Abstract

Adaptation of devices and applications based on contextual information has a great potential to enhance usability and mitigate the increasing complexity of mobile devices. An important topic in context-aware computing is to learn semantic locations and routes of mobile device users. Several batch methods have been proposed to learn these locations. However, such offline methods have very limited usefulness in practice. This paper describes an online adaptive approach to learn user’s semantic locations. The proposed method models user’s GPS data as a mixture of Gaussians, which is updated by an online estimation. The learned Gaussian mixture is then evaluated to determine which components most likely correspond to the important locations based on a priori probabilities. With learned semantic locations, we also propose a minimax criterion to discover user’s frequent transportation routes, which are modeled as sequences of GPS data. Finally, we describe an application of the proposed methods in a cell phone based automatic traffic advisory system.