Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them. First, we present the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories. Based on this distance, we design an anomaly score function to quantify the differences between different types of anomalous trajectories and normal trajectories. We further propose an anomalous trajectory detection and classification (ATDC) method to find anomalies in different anomalous patterns. Finally, we evaluate the proposed ATDC method through extensive experiments on real cab trajectory data. The results show that the proposed approach outperforms existing methods by a significant margin.

中文翻译：

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基于差值和交集距离的异常轨迹检测与分类

异常检测是轨迹数据挖掘中的一个重要问题。已经提出了各种方法来解决这个问题。然而，以前的大多数研究仅关注异常值检测，而很少关注异常轨迹的模式挖掘。异常轨迹的挖掘模式可以揭示这些异常值的潜在机制。本文研究了异常轨迹的四种不同模式，并提出了一种检测和分类它们的方法。首先，我们提出了差异和交集（DIS）距离度量来评估任何两条轨迹之间的相似性。基于这个距离，我们设计了一个异常评分函数来量化不同类型的异常轨迹和正常轨迹之间的差异。我们进一步提出了一种异常轨迹检测和分类（ATDC）方法来发现不同异常模式中的异常。最后，我们通过对真实驾驶室轨迹数据的大量实验来评估所提出的 ATDC 方法。结果表明，所提出的方法明显优于现有方法。