Efficient Trajectory Compression and Range Query Processing

TL;DR

This paper introduces a new trajectory compression algorithm, ROCE, that balances accuracy, speed, and compression rate, along with an efficient range query method for compressed trajectories, improving performance on large GPS datasets.

Contribution

The paper presents ROCE, an online trajectory compression algorithm with error bounds, and a novel range query method tailored for compressed trajectories, along with an efficient indexing structure.

Findings

ROCE achieves a good balance between accuracy, speed, and compression rate.

The RQC range query method effectively handles sparse, compressed trajectory data.

Experimental results show significant performance improvements over existing methods.

Abstract

Nowadays, there are ubiquitousness of GPS sensors in various devices collecting, transmitting and storing tremendous trajectory data. However, such an unprecedented scale of GPS data has posed an urgent demand for not only an effective storage mechanism but also an efficient query mechanism. Line simplification in online mode, searving as a mainstream trajectory compression method, plays an important role to attack this issue. But for the existing algorithms, either their time cost is extremely high, or the accuracy loss after the compression is completely unacceptable. To attack this issue, we propose $\epsilon \_$Region based Online trajectory Compression with Error bounded (ROCE for short), which makes the best balance among the accuracy loss, the time cost and the compression rate. The range query serves as a primitive, yet quite essential operation on analyzing trajectories. Each trajectory is usually seen as a sequence of discrete points, and in most previous work, a trajectory is judged to be overlapped with the query region R iff there is at least one point in this trajectory falling in R. But this traditional criteria is not suitable when the queried trajectories are compressed, because there may be hundreds of points discarded between each two adjacent points and the points in each compressed trajectory are quite sparse. And many trajectories could be missing in the result set. To address this, in this paper, a new criteria based on the probability and an efficient Range Query processing algorithm on Compressed trajectories RQC are proposed. In addition, an efficient index \emph{ASP\_tree} and lots of novel techniques are also presented to accelerate the processing of trajectory compression and range queries obviously. Extensive experiments have been done on multiple real datasets, and the results demonstrate superior performance of our methods.