Road-Aware Indexing for Trajectory Range Queries

Answering spatio-temporal range queries (RQs) on trajectory databases, i.e., finding all trajectories that intersect given ranges, is crucial in many real-world applications. Various kinds of indexes have been proposed to accelerate RQs. However, existing indexes typically use euclidean distance to prune irrelevant regions without concerning the underlying road network information. Nevertheless, as vehicle trajectories are generated on road network edges, the road network could be seen as meta knowledge of trajectories and be used to index and query trajectories. To this end, we propose RP-Tree, a road network-aware partition tree to support efficient RQs. The basic idea is partitioning a road network graph into hierarchical subgraphs and generate a balanced tree structure, where each tree node maintains its associated trajectories. We compactly index the spatio-temporal information of trajectories on the corresponding road network edges. Then, we design efficient search algorithms to support RQs by pruning irrelevant trajectories through subgraph range borders associated with RP-Tree nodes. Last but not least, we scale RP-Tree to very large datasets by devising approximate algorithms with bounded confidence at an interactive speed. Experimental results on three real-world datasets from Porto, Chengdu, and Beijing show that our method outperform baselines by 1 to 2 orders of magnitude.