ABSTRACT Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.

中文翻译：

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车辆为何停下？一种车辆轨迹中停止检测和分类的方法

摘要 轨迹数据挖掘是时空数据挖掘领域中一个活跃的研究领域。轨迹模式挖掘包括一组特定的模式挖掘方法，这些方法作为轨迹上的连续步骤应用，目的是提取和分类重复出现的时空模式。尽管 GIScience 社区对此类方法具有普遍性和频繁使用性，但到目前为止还缺少一种方法论方法，尤其是在使用基于机器学习的分类方法时。当前的工作通过提出和评估基于机器学习的 3 步轨迹数据挖掘方法来弥补这一差距，使用车辆轨迹中停止点的检测和分类作为示例。这项工作详细描述了三个挖掘步骤“停车检测”、“特征提取”和“交通相关和非交通相关停靠点分类”的应用方法，并使用一个基于机器学习的分类算法评估了六种基于机器学习的分类算法。真实世界数据集包含 15,498 条车辆轨迹，检测到 5,899 个停靠点（其中 2,032 个手动分类）。由于其示例性，所提出的方法适合作为类似轨迹数据挖掘问题的蓝图。