MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors

Abstract

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection.

Smoothing Outlier Scores Is All You Need to Improve Outlier Detectors

“…O UTLIERS are objects that significantly deviate from other objects. Outliers can indicate useful information, which can be applied in applications such as fraud detection [1], [2], abnormal time series [3], [4], and traffic patterns [5], [6]. Outliers can also be harmful because they are generally unwanted, can be considered errors, and may bias statistical analysis for applications like clustering [7], [8].…”