这篇论文提供了16个带噪声的轨迹数据集和9种驻留点检测算法评估，发现现有方法在真实噪声下效果差，而他们的新方法提升明显，对做轨迹分析的人很有参考价值。
该论文引入了16个大规模模拟数据集，每个数据集包含数千个智能体的轨迹及带注释的驻留点，并涵盖不同的轨迹噪声水平。作者评估了9种驻留点检测算法，包括现有最优方法和新提出的方法。结果显示，现有算法在真实噪声条件下表现较差，而提出的无监督方法有显著改进，监督方法则大幅超越现有基线。该论文指出，这些数据集和方法仅作为未来研究的起点。
Staypoint Detection from Noisy Trajectory Data [Experiment Paper]
Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.