One-Pass Error Bounded Trajectory Simplification

TL;DR

This paper introduces two efficient one-pass algorithms, OPERB and OPERB-A, for trajectory data simplification that reduce storage and processing needs while maintaining error bounds, validated on real datasets.

Contribution

The paper presents novel one-pass, error-bounded trajectory simplification algorithms OPERB and OPERB-A, suitable for resource-constrained mobile sensors.

Findings

OPERB and OPERB-A are efficient in processing large trajectory datasets.

Both algorithms effectively reduce data size while respecting error bounds.

Experimental results confirm the algorithms' practicality and effectiveness.

Abstract

Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are an effective approach to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. However, existing LS algorithms are not sufficient for the needs of sensors in mobile devices. In this study, we first develop a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once. We then propose an aggressive one-pass error bounded trajectory simplification algorithm (OPERB-A), which allows interpolating new data points into a trajectory under certain conditions. Finally, we experimentally verify that our approaches (OPERB and OPERB-A) are both efficient and effective, using four real-life trajectory datasets.