VisitFrequency-Diffusion: Leveraging Recurrent Visits for Long-Term Individual Trajectory Forecasting

Individual trajectory prediction plays a crucial role in intelligent transportation systems. While existing methods demonstrate strong performance in short-term forecasting (e.g., minute-level predictions), they are limited in modeling long-term patterns (day-level predictions). The key challenge is capturing both the periodic regularity and stochastic variability of urban mobility. To bridge this gap, we propose VF-Diffusion, a novel framework for long-term individual trajectory prediction with three key innovations: (1) A direction-sensitive diffusion model that generates baseline trajectories by learning motion trends; (2) A trajectory rectification module that refines spatial displacements using historical median coordinates; and (3) A frequency-sensitive mechanism that identifies high-frequency visit locations, predicts their temporal sequences via an ensemble model, and integrates them with the baseline trajectory. By combining generative modeling with a frequency-sensitive mechanism, VF-Diffusion fills a critical gap in existing methods, offering the ability to predict new visiting areas and improve trajectory accuracy. Extensive experiments on Beijing Wi-Fi trajectory data show that our method outperforms four baselines, achieving about 90% accuracy for predictions within a 1 km threshold. It particularly excels in areas with frequent and periodic visits. This framework advances trajectory prediction by enabling multi-day forecasting, a previously underexplored capability, and offers practical solutions for enhancing smart city infrastructure.