One-Pass Trajectory Simplification Using the Synchronous Euclidean Distance

TL;DR

This paper introduces two novel one-pass trajectory simplification algorithms using synchronous Euclidean distance, significantly improving efficiency and compression ratios over existing methods for spatio-temporal data.

Contribution

The paper presents the first one-pass algorithms using SED for trajectory simplification, employing a new spatio-temporal cone intersection technique.

Findings

Algorithms are on average 3 times faster than SQUISH-E.

Compression ratios are 19.6% better than DPSED.

Algorithms outperform SQUISH-E in compression by 21.1% and 42.4%.

Abstract

Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. To attack this issue, one-pass line simplification (LS) algorithms have are been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (SED), and cannot support spatio-temporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (CISED-S and CISED-W) using SED, based on a novel spatio-temporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms CISED-S and CISED-W are on average 3 times faster than SQUISH-E (the most efficient existing LS algorithm using SED). In terms of compression ratios, algorithms CISED-S and CISED-W are comparable with and 19.6% better than DPSED (the most effective existing LS algorithm using SED) on average, respectively, and are 21.1% and 42.4% better than SQUISH-E on average, respectively.