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

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.The authors would like to thank Dmitry Duda for his work in the implementation of the application described in Section 4. This work has been partially supported by the project Espãna Virtual, led by DEIMOS Space and funded by CDTI as part of the Ingenio 2010 program. It has been also partially supported by the Spanish Ministry of Science and Innovation through the CONSEQUENCE project (TEC2010-20572-C02-01) and partially founded by UC3M and DGUI in the framework of the project CCG10-UC3M/TIC-4992