Vehicle trajectories are one of the most important data in location-based services. The quality of trajectories directly affects the services. However, in the real applications, trajectory data are not always sampled densely. In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory. Most existing works solve the problem with-out using those informative historical data or solve it in an empirical way. We claim that a data-driven and probabilistic approach is actually more suitable as long as data sparsity can be well handled. We propose a novel route recovery system in a fully probabilistic way which incorporates both temporal and spatial dynamics and addresses all the data sparsity problem introduced by the probabilistic method. It outperforms the existing works with a high accuracy (over 80%) and shows a strong robustness even when the length of routes to be recovered is very long (about 30 road segments) or the data is very sparse.

Filed under: Mining Rich Data Types