{
  "annotation_id": "0a4c2457-f159-4329-ac22-96cdbcc30405",
  "date_created": "2026-02-17T05:53:15.041000Z",
  "date_modified": "2026-02-17T05:53:15.041000Z",
  "file_hash": "8f775b24f6cc677cb78315561bb371b5b99a5183c35af48a62f58369ca3e08b3",
  "private": false,
  "record": {
    "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\u0027s superior accuracy.",
    "arxiv_id": "2601.08013",
    "authors": [
      "Nairui Liu",
      "Fang He",
      "Xindi Tang"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Beyond the Next Port: A Multi-Task Transformer for Forecasting Future Voyage Segment Durations",
    "url": "https://arxiv.org/abs/2601.08013",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4d4bf33c-f8b9-4863-8a2e-5fcd04b23c65",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}