Beyond the Next Port: A Multi-Task Transformer for Forecasting Future Voyage Segment Durations

• URL: http://arxiv.org/abs/2601.08013v1
• Date: Mon, 12 Jan 2026 21:32:05 GMT
• Title: Beyond the Next Port: A Multi-Task Transformer for Forecasting Future Voyage Segment Durations
• Authors: Nairui Liu, Fang He, Xindi Tang,
• Abstract summary: The study reformulates future-port ETA prediction as a segment-level time-series forecasting problem.<n>We develop a transformer-based architecture that integrates historical sailing durations, destination port congestion, and static vessel descriptors.<n>The proposed framework employs a causally masked attention mechanism to capture long-range temporal dependencies and a multi-task learning head to jointly predict segment sailing durations and port congestion states.
• Score: 3.9443085703523706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate forecasts of segment-level sailing durations are fundamental to enhancing maritime schedule reliability and optimizing long-term port operations. However, conventional estimated time of arrival (ETA) models are primarily designed for the immediate next port of call and rely heavily on real-time automatic identification system (AIS) data, which is inherently unavailable for future voyage segments. To address this gap, the study reformulates future-port ETA prediction as a segment-level time-series forecasting problem. We develop a transformer-based architecture that integrates historical sailing durations, destination port congestion proxies, and static vessel descriptors. The proposed framework employs a causally masked attention mechanism to capture long-range temporal dependencies and a multi-task learning head to jointly predict segment sailing durations and port congestion states, leveraging shared latent signals to mitigate high uncertainty. Evaluation on a real-world global dataset from 2021 demonstrates the proposed model consistently outperforms a comprehensive suite of competitive baselines. The result shows a relative reduction of 4.85% in mean absolute error (MAE) and 4.95% in mean absolute percentage error (MAPE) compared with sequence baseline models. The relative reductions with gradient boosting machines are 9.39% in MAE and 52.97% in MAPE. Case studies for the major destination port further illustrate the model's superior accuracy.

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