A User-Centered Location Model
Personal and Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Some help on the way: opportunistic routing under uncertainty
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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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.