Anomalous trajectory detection is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods mainly focus on the differences of a new trajectory and the historical trajectory with density and isolation techniques, which may suffer from the following two disadvantages. (1) They cannot capture the sequential information of the trajectory well. (2) They cannot make use of the common information of the trajectory points. To overcome the above shortcomings, we propose a novel method called A nomalous T rajectory D etection using R ecurrent N eural N etwork (ATD − RNN) which characterizes the trajectory with the learned trajectory embedding. The trajectory embedding can capture the sequential information of the trajectory and depict the internal characteristics between abnormal and normal trajectories. In order to learn the high-quality trajectory embedding, we further propose an attention mechanism to aggregate the long sequential information. Furthermore, to alleviate the data sparsity problem, we augment the datasets between a source and a destination by taking the relevant trajectories into consideration simultaneously. Extensive experiments on real-world datasets validate the effectiveness of our proposed methods.

中文翻译：

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嵌入地理信息以进行异常轨迹检测

轨迹探测异常是轨迹采矿领域的关键任务。传统的异常轨迹检测方法主要着眼于新轨迹与历史轨迹在密度和隔离技术上的差异，可能存在以下两个缺点。（1）他们无法很好地捕获轨迹的顺序信息。（2）他们不能利用轨迹点的公共信息。为了克服上述缺点，提出了一种所谓的新方法甲nomalous Ť rajectory d etection使用ř ecurrent Ñ eural Ñ etwork（甲Ť d - [R ÑN）通过学习的轨迹嵌入来表征轨迹。轨迹嵌入可以捕获轨迹的顺序信息，并描绘异常和正常轨迹之间的内部特征。为了学习高质量的轨迹嵌入，我们进一步提出了一种关注机制来聚合长序列信息。此外，为了缓解数据稀疏性问题，我们通过同时考虑相关轨迹来扩充源和目的地之间的数据集。在真实数据集上的大量实验验证了我们提出的方法的有效性。