PRESS: A Novel Framework of Trajectory Compression in Road Networks

TL;DR

PRESS introduces a new trajectory compression framework that effectively reduces storage costs by separating spatial and temporal data, supporting queries without full decompression, and outperforming existing methods on real datasets.

Contribution

The paper presents PRESS, a novel framework that separates spatial and temporal trajectory representations and introduces hybrid compression algorithms, improving efficiency and query support.

Findings

Significantly reduces storage costs of trajectory data.

Supports spatial-temporal queries without full decompression.

Outperforms existing approaches in experimental evaluations.

Abstract

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.