Path Recovery Of Sparse Location Trajectory

The application of location data acquisition technology has given rise to a newspectrum of location-based services, which have accumulated a huge collection oflocation trajectories. But in this way, the sampling rate of generated vehicleinformation is always low, so that most details of their movement are lost whichmakes a lot of uncertainties when matching them to the real road. HRIS is asystematic solution for the map matching of low-sampling-rate trajectories, whichreduce the uncertainty in such kind of trajectories and infer its possible routes. In thispaper, we proposed an improvement of the system, so that it can solve the pathrecovery of sparse location trajectory.This paper used a distributed way to prepare the historical trajectories, includingextract, split and map-matching, and evaluated the performance of step, then used theresult to construct a landmark graph and access the parameter.This paper analyzed and designed the related components of the path recoverysystem for sparse location trajectory and used the Z language to describe them.This paper implemented the path recovery system for sparse location trajectories,and make use of the landmark graph and the historical trajectories to reduce theuncertainty in such kind of trajectories. Finally, we used a real data set to evaluate thesystem performance and made the prospects of the future work.