提出物理感知的轨迹压缩框架，显著提升多维轨迹压缩效率与精度。
PILOT-C: Physics-Informed Low-Distortion Optimal Trajectory Compression
- 基于频域物理建模与误差约束优化，支持任意维度轨迹独立压缩。
- 相比CISED-W平均压缩率高19.2%，误差降低32.6%。
- 3D轨迹压缩效率比SQUISH-E高49%，计算复杂度不变。
具备位置感知的设备持续生成海量轨迹数据，亟需高效压缩方案。线简化是常见方法，但通常仅适用于二维轨迹，且忽略时间同步与运动连续性。本文提出PILOT-C，一种融合频域物理建模与误差有界优化的新型轨迹压缩框架。不同于现有线简化方法，PILOT-C可支持任意维度轨迹（包括3D），通过独立压缩各空间轴实现。在四个真实数据集上评估显示，其性能优越：相比当前最先进的基于SED的算法CISED-W，平均压缩率提升19.2%，误差平均降低32.6%；在3D轨迹场景下，相较最高效的算法SQUISH-E，压缩率提升49%的同时保持相同计算复杂度。
原文摘要 · Abstract (English)
Location-aware devices continuously generate massive volumes of trajectory data, creating demand for efficient compression. Line simplification is a common solution but typically assumes 2D trajectories and ignores time synchronization and motion continuity. We propose PILOT-C, a novel trajectory compression framework that integrates frequency-domain physics modeling with error-bounded optimization. Unlike existing line simplification methods, PILOT-C supports trajectories in arbitrary dimensions, including 3D, by compressing each spatial axis independently. Evaluated on four real-world datasets, PILOT-C achieves superior performance across multiple dimensions. In terms of compression ratio, PILOT-C outperforms CISED-W, the current state-of-the-art SED-based line simplification algorithm, by an average of 19.2%. For trajectory fidelity, PILOT-C achieves an average of 32.6% reduction in error compared to CISED-W. Additionally, PILOT-C seamlessly extends to three-dimensional trajectories while maintaining the same computational complexity, achieving a 49% improvement in compression ratios over SQUISH-E, the most efficient line simplification algorithm on 3D datasets.
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