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

• URL: http://arxiv.org/abs/2606.08633v1
• Date: Sun, 07 Jun 2026 13:55:11 GMT
• Title: Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
• Abstract summary: This paper investigates joint long-horizon vessel trajectory and destination forecasting with reasoning-capable large language models.<n>It develops a Maritime LLM post-training framework based on Reinforcement Learning with Verifiable Reward (RLVR)<n> Experimental results show that RLVR-trained LLMs substantially improve over zero-shot LLMs and representative deep learning baselines.
• Score: 23.05328480428193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied. Existing deep learning methods mainly focus on short- and mid-term coordinate extrapolation and often struggle to preserve route feasibility and destination correctness over extended horizons. This paper investigates joint long-horizon vessel trajectory and destination forecasting with reasoning-capable large language models, and develops a Maritime LLM post-training framework based on Reinforcement Learning with Verifiable Reward (RLVR). An AIS-based benchmark is constructed with 60-day historical trajectories and 30-day forecasting horizons, where trajectories are converted into semantic textual representations for RL prompt construction. RLVR aligns LLMs with maritime forecasting objectives by enforcing physical validity, providing early-weighted trajectory supervision, and evaluating destination correctness through hierarchical matching and curriculum learning. Experimental results show that RLVR-trained LLMs substantially improve over zero-shot LLMs and representative deep learning baselines, especially on destination-related metrics. Among the evaluated RLVR-trained variants, 4B LLMs achieve the best overall performance, suggesting that reward-compatible optimization and task-specific capacity matching are more important than simply using larger 8B or 14B LLMs. The results also show that LSTM remains a strong deep learning baseline under limited fine-tuning data, while Transformer-style spatio-temporal models typically require larger datasets and richer structured inputs. Overall, this work advances semantic, verifier-aligned maritime forecasting for operational decision support.

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