🤖 AI Summary
This study addresses the limitation of existing vessel estimated time of arrival (ETA) models, which are typically restricted to the next port of call and rely heavily on real-time AIS data, thereby struggling to forecast transit durations for future voyage segments. To overcome this, the authors formulate future-port ETA prediction as a segment-level time series forecasting problem and propose a Transformer-based multi-task learning framework that jointly predicts segment-wise sailing durations and port congestion states. A novel causal masked attention mechanism is introduced, coupled with shared latent representations, to mitigate the high uncertainty inherent in long-horizon predictions. The model integrates historical sailing durations, proxy indicators of port congestion, and static vessel attributes. Evaluated on a global real-world dataset from 2021, it significantly outperforms baseline methods, reducing MAE and MAPE by 4.85% and 4.95% compared to sequential models, and by 9.39% and 52.97% against gradient boosting machines, respectively.
📝 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.