🤖 AI Summary
Existing trajectory clustering methods are highly sensitive to transient anomalies, often fragmenting trajectories due to local perturbations—thereby compromising cluster integrity, stability, and interpretability. To address this, we propose a robust trajectory clustering framework built upon segment-based DBSCAN, supporting both full-trajectory and sliding-window subtrajectory modeling. We introduce the Mean Absolute Deviation (MAD) to quantitatively measure local perturbation intensity, enabling selective suppression of transient deviations. Furthermore, we design a motion-history-driven, event-triggered segment splitting and merging mechanism to mitigate over-segmentation induced by anomalies. Experiments on real-world trajectory datasets demonstrate that our method significantly improves clustering stability, parameter robustness, and pattern consistency, outperforming state-of-the-art trajectory clustering algorithms.
📝 Abstract
Clustering algorithms group data points by characteristics to identify patterns. Over the past two decades, researchers have extended these methods to analyze trajectories of humans, animals, and vehicles, studying their behavior and movement across applications. This paper presents whole-trajectory clustering and sub-trajectory clustering algorithms based on DBSCAN line segment clustering, which encompasses two key events: split and merge of line segments. The events are employed by object movement history and the average Euclidean distance between line segments. In this framework, whole-trajectory clustering considers entire entities' trajectories, whereas sub-trajectory clustering employs a sliding window model to identify similar sub-trajectories. Many existing trajectory clustering algorithms respond to temporary anomalies in data by splitting trajectories, which often obscures otherwise consistent clustering patterns and leads to less reliable insights. We introduce the stable trajectory clustering algorithm, which leverages the mean absolute deviation concept to demonstrate that selective omission of transient deviations not only preserves the integrity of clusters but also improves their stability and interpretability. We run all proposed algorithms on real trajectory datasets to illustrate their effectiveness and sensitivity to parameter variations.