PRESS: A Novel Framework of Trajectory Compression in Road Networks

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.Comment: 27 pages, 17 figure

Quick Map Matching Using Multi-Core CPUs

The ACM SIGSPATIAL Cup 2012 is about map matching, a problem of correctly matching a sequence of GPS sampling points to the roads on a digital map. This paper describes one of the winning submissions of the competition. The approach applies multi-threading technology to map matching in order to reduce running time and we propose an improvement to the Hidden Markov Model (HMM) map matching algorithm