Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node-Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time-Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T-Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

中文翻译：

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一种基于节点序列层次图的轨迹相似度度量方法

轨迹相似度测量是轨迹数据挖掘中的一项基本而重要的任务，在过去的几十年中引起了广泛的研究。最近的工作主要集中在轨迹的序列和层次结构属性上，以构建相似性测量。然而，这些方法忽略了有关访问位置的用户信息，例如语义和时间分布。鉴于此，本文提出了一种基于节点序列层次图（NSHD）框架的新型轨迹相似度测量。我们首先提出了时间加权停留点检测（TWSPD）方法来更准确地提取用户的真实访问位置。然后，通过对用户的停留点进行聚类得到有向图的节点，有向图的边是用户在这些节点之间移动的顺序信息。提出了先进的地球移动器距离（AEMD）来衡量用户之间的节点相似性，同时考虑访问时间分布和语义信息。节点和序列相似度都用于计算相似度分数以获得最终的轨迹相似度度量。在 Geolife 和 T-Drive 数据集上的实验表明，我们提出的方法提供了具有竞争力的性能，平均倒数排名值分别达到 96.01 和 81.26%，比相关轨迹相似性测量结果高出 10% 和 15% 以上。