TrajLearn: Trajectory Prediction Learning using Deep Generative Models

• URL: http://arxiv.org/abs/2501.00184v1
• Date: Mon, 30 Dec 2024 23:38:52 GMT
• Title: TrajLearn: Trajectory Prediction Learning using Deep Generative Models
• Authors: Amirhossein Nadiri, Jing Li, Ali Faraji, Ghadeer Abuoda, Manos Papagelis,
• Abstract summary: Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data. To address these challenges, we introduce TrajLearn, a novel model for trajectory prediction. TrajLearn predicts the next $k$ steps by integrating a customized beam search for exploring multiple potential paths.
• Score: 4.097342535693401
• License:
• Abstract: Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. Deep learning approaches have become key in this area, utilizing large-scale trajectory datasets to model movement patterns, but face challenges in managing complex spatial dependencies and adapting to dynamic environments. To address these challenges, we introduce TrajLearn, a novel model for trajectory prediction that leverages generative modeling of higher-order mobility flows based on hexagonal spatial representation. TrajLearn predicts the next $k$ steps by integrating a customized beam search for exploring multiple potential paths while maintaining spatial continuity. We conducted a rigorous evaluation of TrajLearn, benchmarking it against leading state-of-the-art approaches and meaningful baselines. The results indicate that TrajLearn achieves significant performance gains, with improvements of up to ~40% across multiple real-world trajectory datasets. In addition, we evaluated different prediction horizons (i.e., various values of $k$), conducted resolution sensitivity analysis, and performed ablation studies to assess the impact of key model components. Furthermore, we developed a novel algorithm to generate mixed-resolution maps by hierarchically subdividing hexagonal regions into finer segments within a specified observation area. This approach supports selective detailing, applying finer resolution to areas of interest or high activity (e.g., urban centers) while using coarser resolution for less significant regions (e.g., rural areas), effectively reducing data storage requirements and computational overhead. We promote reproducibility and adaptability by offering complete code, data, and detailed documentation with flexible configuration options for various applications.

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