Trajectory Anomaly Detection Via Spatial–temporal Complementary Reconstruction Diffusion Model

Detecting anomalies in spatiotemporal trajectory data is pivotal for understanding urban mobility and identifying irregular travel behaviors. Existing methods primarily employ reconstruction-based frameworks to encode trajectories into latent representations and then reconstruct the original trajectory. However, they often struggle to capture the complex spatiotemporal dependencies inherent in vehicle movements because they rely heavily on trajectory embeddings. To address this challenge, we introduce CRDiff, a novel trajectory anomaly detection framework via spatial-temporal complementary reconstruction diffusion model. Specifically, we propose a complementary trajectory masking mechanism that partitions trajectory points into observed and unobserved subsets, formulating the problem as a conditional imputation task. By utilizing dual complementary branches, the model comprehensively leverages context from the entire trajectory to capture intricate correlations. Furthermore, we incorporate a Travel Pattern-aware Trajectory Encoder to extract macro-level movement semantics, coupled with a spatiotemporal trajectory denoiser to model fine-grained spatiotemporal dependencies. Finally, anomalies are identified through an ensemble of multi-step diffusion reconstruction errors, significantly enhancing detection robustness. Extensive experiments on two public vehicle trajectory datasets demonstrate that CRDiff outperforms state-of-the-art baselines in detection accuracy.