CF-TS: A General Coarse-to-Fine Method for Trajectory Simplification

Trajectory simplification aims to reduce the sizes of trajectories while preserving as much information as possible, thus improving storage, processing, and transmission efficiency. Despite substantial progress of existing studies, three major limitations remain: 1) inefficient initialization, 2) rigid pre-defined thresholds, and 3) inadequate consideration of trajectory attributes. To address these challenges, we propose a novel Coarse-to-Fine framework for Trajectory Simplification, termed CF-TS, consisting of two stages. In the coarse-grained stage, CF-TS employs a customized variant of Douglas-Peucker as a robust warm-start in a lightweight way. In the fine-grained stage, Monte Carlo Tree Search iteratively refines the trajectory in regions requiring optimization based on a candidate set, which leverages a broader spectrum of features, including smoothness and direction error, to ensure a more accurate representation of original trajectory. Extensive experiments on real-world datasets demonstrate that CF-TS outperforms state-of-the-art methods in both effectiveness and efficiency, establishing itself as a novel paradigm.