Indexing moving object trajectories with hilbert curves
Reaz Uddin, Chinya V. Ravishankar, Vassilis J. Tsotras · 2018
Efficiently querying large trajectory datasets is a challenge of growing importance. Abstracting trajectory segments with minimum bounding boxes and indexing them in R-Trees results in a high false positive rate due to high dead space. Space filling curves (SFCs), which have excellent locality preserving and dimensionality reduction properties, have been shown to be effective for indexing points in space. However, they can yield a high false positive count and slow query times if used to index trajectory segments. Our work shows how to use SFCs to index trajectory polylines. In our experiments, the proposed method runs 2--15 times faster than other state-of-the-art approaches.