Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.

识别轨迹数据中运动结束和重要停止开始的部分是一项基本但基本的任务，它会影响任何移动分析过程的质量。研究人员和从业者采用的许多现有解决方案中的大多数只是基于固定的空间和时间阈值，说明移动对象何时保持静止很长时间，但这些阈值仍然作为静态参数供用户猜测。在这项工作中，我们从多粒度的角度研究轨迹分割，寻找对问题的更好理解，并寻找一种自动的、用户自适应的且基本上无参数的解决方案，该解决方案可以根据所研究的特定用户灵活调整分割标准和到他们穿越的地理区域。对真实数据的实验，