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

TL;DR

This paper introduces a transformer-based multi-task model for predicting future voyage segment durations, addressing the limitations of traditional ETA models that focus only on immediate ports and rely on unavailable real-time data.

Contribution

The study develops a novel multi-task transformer architecture that integrates historical data and port congestion proxies for improved future voyage duration forecasting.

Findings

Model outperforms baseline methods with up to 7% reduction in MAE.

Achieves significant improvements in MAPE and RMSE metrics.

Case study confirms superior accuracy at a major destination port.

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 4.70% in mean absolute error (MAE), 4.95% in mean absolute percentage error (MAPE) and 2.59% in root mean squared error (RMSE) compared with sequential deep learning models. The relative reductions compared with gradient boosting machines are 7.03% in MAE, 39.49% in MAPE and 4.37% in RMSE. The case study conducted on one major destination port further illustrates the model's superior accuracy.