发表机构
Emory University（埃默里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对停留点检测缺乏标准基准及算法未系统评估的问题，引入16个大规模模拟数据集，评估九种停留点检测算法，发现现有算法在现实噪声条件下不佳，所提无监督方法有改进，监督方法优于基线，为后续研究提供了起点。
AI 中文摘要
从原始轨迹数据中检测停留点对众多空间计算应用至关重要，它将地理位置的原始数字序列转换为语义上有意义的位置。然而，停留点检测缺乏标准基准，现有算法也未得到系统评估，原因是没有公开数据集能同时提供原始个体轨迹和真实停留点注释。本文通过两个关键贡献解决了这一局限：一是引入16个大规模模拟数据集，涵盖不同轨迹噪声水平下数千个带注释停留点的智能体；二是评估九种停留点检测算法，分析其对噪声的鲁棒性。评估发现现有最先进算法在现实噪声条件下表现不佳，而本文提出的无监督方法有显著改进，监督方法也大幅优于现有基线。这些数据集和方法只是停留点检测未来研究的起点。
英文摘要
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.
