Towards Trajectory Anomaly Detection: a Fine-Grained and Noise-Resilient Framework

Trajectory anomaly detection aims to identify patterns in trajectory data that deviate significantly from normal behavior, such as taxi detours, and plays a crucial role in urban computing. However, real-world trajectories are inherently complex, containing diverse anomalies and unavoidable noise. Existing research mainly focuses on coarse-grained trajectory anomalies, such as detour and switch anomalies, while paying limited attention to fine-grained trajectory anomalies, such as time and loop anomalies. Furthermore, they tend to disregard the impact of inherent noise in trajectories. As a result, there remains a gap in developing robust models with strong generalization capabilities to effectively detect fine-grained trajectory anomalies, even in noisy environments.