Improving persistence based trajectory simplification

In this paper, we propose a novel linear time online algorithm for simplification of spatial trajectories. Trajectory simplification plays a major role in movement data analytics, in contexts such as reducing the communication overhead of tracking applications, keeping big data collections manageable, or harmonizing the number of points per trajectory. We follow the framework of topological persistence in order to detect a set of important points for the shape of the trajectory from local geometry information. Topological is meant in the mathematical sense in this paper and should not be confused with geographic topology. Our approach is able to prune pairs of non-persistent features in angle-representation of the trajectory. We show that our approach outperforms previous work, including multiresolution simplification (MRS) by a significant margin over a wide range of datasets without increasing computational complexity. In addition, we compare our novel algorithm with Douglas Peucker which is widely respected for its high-quality simplifications. We conclude that some datasets are better simplified using persistence-based methods and others are more difficult, but that the variations between the three considered variants of persistence-based simplification are small. In summary, this concludes that our novel pruning rule Segment-Distance Simplification (SDS) leads to more compact simplification results compared to β-pruning persistence and multiresolution simplification at similar quality levels in comparison to Douglas Peucker over a wide range of datasets.