Forecasting Moving Object Position based on Temporal Patterns

Knowing the future position of a mobile user bring huge benefit to applications in the field of location-based services (LBSs). One of the major advantages is that helps LBSs provide targeted content or disseminate advertisement to mobile users at the right time right place. This research introduces a new technique for forecasting future position. It is based on temporal patterns discovered from the historical user mobility. The algorithm is validated in consideration of precision and recall and spatial granularity. The experimental results show that the proposed technique outperforms the existing one. With this research achievement, we give an efficient support to the LBSs provider in monitoring user intelligently and sending information to user in a push-driven fashion. Apart from the support of timely and desired services and enhanced automation, the technique helps overcome some existing issues such as network flooding due to the massive tracking of users, the latencies of the positioning systems in providing and information delivery. Accordingly, the positioning is more reliable, which enables the service provider to effectively and efficiently offer location-based services with high frequency.