Management And Prediction Of Moving Objects Under Location Uncertainty

In spatio-temporal systems, precise location data is desirable but often not available due to obfuscation, privacy, hardware inaccuracies, and other factors. Progress has been made in research which deals with the uncertainty of moving objects' location data. However, much of the existing work does not always consider factors such as constraints imposed by the topology of road networks, and harmonic integration between past movements, current, and prospective imprecise positions. In this thesis, we propose an approach that utilizes time, distance, and connectivity constraints of a road network to infer a moving object's past, present, and future locations more precisely when its exact location data is not available. The experimental results using real GPS trajectories confirm the efficiency of our proposed solution for reducing uncertainty and inferring historical, and future locations.