Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models

Researchers develop a large language model framework for predicting vessel trajectories and destinations up to 30 days in advance using reinforcement learning with verifiable rewards. The approach outperforms traditional deep learning methods by maintaining route feasibility and destination accuracy over extended maritime forecasting horizons.

This research addresses a significant gap in maritime logistics by extending trajectory prediction capabilities from short-term to month-level forecasting. Traditional deep learning approaches struggle with long-horizon predictions because they prioritize coordinate accuracy without ensuring physical feasibility or realistic destination outcomes. The Maritime LLM framework leverages semantic reasoning to understand shipping patterns contextually rather than merely extrapolating numerical coordinates.

The development of the RLVR (Reinforcement Learning with Verifiable Reward) training methodology represents an important advancement in applying LLMs to domain-specific operational problems. By encoding AIS data into textual representations and training models with hierarchical destination matching and curriculum learning, the framework aligns LLM capabilities with real-world maritime constraints. The finding that 4B parameter models outperform larger 8B and 14B variants challenges conventional assumptions about scale in specialized applications, suggesting that task-specific architecture and reward alignment matter more than raw model size.

For the maritime and logistics industries, this technology enables more reliable operational planning, risk assessment, and resource allocation across shipping networks. Accurate month-level forecasting reduces inefficiencies in route planning and helps identify potential port congestion or maritime hazards earlier. The comparison showing LSTM remains competitive under limited training data while Transformer models require substantially more data informs infrastructure investment decisions for organizations developing maritime AI systems.

The work establishes a template for applying reasoning LLMs to time-series prediction in specialized domains where semantic understanding and physical constraints matter equally. Future iterations may incorporate additional variables like weather patterns, fuel costs, and regulatory factors to provide more comprehensive decision support.

• →LLM-based maritime trajectory prediction achieves superior 30-day forecasting by preserving route feasibility and destination correctness compared to traditional deep learning methods.
• →Smaller 4B LLMs with task-specific reward optimization outperform larger models, indicating that architecture alignment and training methodology exceed raw scale benefits.
• →RLVR framework demonstrates practical application of verifiable rewards in aligning LLMs with domain-specific constraints in operational logistics.
• →LSTM remains competitive for maritime forecasting with limited data, while Transformer architectures require larger datasets for effective performance.
• →Semantic textual representations of AIS data enable reasoning-based forecasting applicable across specialized time-series prediction domains.