Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining

Abstract

In recent years, with the advances in mobile communication and growing popularity of the fourth-generation mobile network along with the enhancement in location positioning techniques, mobile devices have generated extensive spatial trajectory data, which represent the mobility of moving objects. New services are emerged to serve mobile users based on their predicted locations. Most of the existing studies on location prediction were focused on predicting the next location of a user, which is regarded as short-term next location prediction. While more advanced location-based services could be enabled for the users if long-term location prediction could be achieved, the existing methods constrained in next-location prediction are not applicable for long-term prediction scenario. In this paper, we propose a novel prediction framework named LSTM-PPM that utilises deep learning and periodic pattern mining for long-term prediction of user locations. Our framework devises the ideology from natural language model and uses multi-step recursive strategy to perform long-term prediction. Furthermore, the periodic pattern mining technique is utilized to reduce the accumulated loss in the multi-step strategy. Through empirical evaluation on a real-life trajectory dataset, our proposed approach is shown to provide effective performance in long-term location prediction. To the best of our knowledge, this is the first work addressing the research topic on long-term user location prediction.