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Anomalous Trajectory Detection and Classification Based on Difference and Intersection Set Distance
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DOI：10.1109/TVT.2020.2967865.png)
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摘要
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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.
Keyword:
Trajectory data
anomalous trajectory detection
distance metric
anomalous pattern classification
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IF:
7.1
论文数:
1.8W
被引数:
6.6W
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Hierarchical trajectory clustering for spatio-temporal periodic pattern mining基于层次轨迹聚类的时空周期模式挖掘
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分享收藏Trajectory outlier detection approach based on common slices sub-sequence.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
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