Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

Sensors

Alicia Rodriguez-Carrion¹  Carlos Garcia-Rubio²  Celeste Campo²  Alberto Cortés-Martín²  Estrella Garcia-Lozano² 

[1] Department of Telematic Engineering, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain;

关键词: GSM-based location;    prediction;    LZ;    LeZi Update;    Active LeZi;    recommender system;    ambient intelligence;    ubiquitous computing;   

DOI  :  10.3390/s120607496

来源: mdpi

【 摘 要 】

Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.