ABSTRACT The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.

中文翻译：

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从一小组 GPS 轨迹到详细的运动概况：量化个性化旅行相关的运动多样性

摘要 无处不在的个人传感设备使得收集大量、多样和细粒度的时空数据集成为可能。这些数据集促进了从交通监控和管理到基于位置的服务的众多应用。最近，基于细粒度轨迹数据为个性化服务分析个人运动的兴趣越来越大。大多数方法通过分析粗略的位置信息（例如，经常访问的地方）来识别用户最具代表性的路径。然而，即使对于具有相同出发地和目的地的旅行，个人也会表现出多种行为（例如，绕道上学、在超市短暂停留）。表征和比较个体变异性的能力 细粒度的运动行为可以极大地支持基于位置的服务和智能空间采样策略。我们提出了一个 TRip 多样性度量——TRIM——它量化了用户在起点和终点之间的路径选择的规律性。TRIM 有效地捕捉了给定起点和终点对之间所采用路径的多样性程度，并识别具有不同运动模式的用户，同时促进用户之间运动行为变化的比较。我们使用合成和真实数据集以及跨地域的实验显示了我们方法的有效性。TRIM 有效地捕捉了给定起点和终点对之间所采用路径的多样性程度，并识别具有不同运动模式的用户，同时促进用户之间运动行为变化的比较。我们使用合成和真实数据集以及跨地域的实验显示了我们方法的有效性。TRIM 有效地捕捉了给定起点和终点对之间所采用路径的多样性程度，并识别具有不同运动模式的用户，同时促进用户之间运动行为变化的比较。我们使用合成和真实数据集以及跨地域的实验显示了我们方法的有效性。