Trajectory Similarity Estimation via Geometry-Aligned InfoNCE: A Self-Supervised Framework With Multi-Factor Features and Robust Augmentations

Reliable trajectory similarity estimation underpins applications such as map matching, mobility anomaly detection, and travel behavior analysis. However, traditional distance-based methods are computationally expensive and sensitive to irregular sampling, while learning-based approaches, though more efficient, often neglect geometric consistency and degrade under sparse or noisy trajectory data. This paper presents GeoCPC-TrajSim, a self-supervised framework for trajectory similarity estimation that employs an Information Noise-Contrastive Estimation (InfoNCE) objective aligned with shape geometry through a shape-consistent positive–negative sampling strategy. To evaluate and preserve this alignment, we establish the Trajectory Geometry-Consistency Benchmark (TGCB), a unified metric space for both optimization and assessment based on Fréchet, Hausdorff, and directional dispersion measures. A Spatio-Temporal Augmentation and Alignment Module (SAAM) introduces two augmentations: sliding-window interpolation, which densifies sparse trajectories while maintaining curvature continuity, and detour offset injection, which simulates realistic Global Navigation Satellite System (GNSS) drift. A Multi-Factor Feature Extraction Module (MFEM) enriches the representation with nine kinematic and directional descriptors beyond conventional spatial–temporal inputs, mitigating the information loss inherent in sparse trajectories. Experiments on Grab-Posisi and GeoLife datasets demonstrate that GeoCPC-TrajSim improves accuracy, robustness, and efficiency in trajectory similarity estimation while reducing training cost by over 70% compared with recent baselines.