Deep Learning-Based Vessel Traffic Prediction Using Historical Density and Wave Features

Dogan Altan, Dusica Marijan, Tetyana Kholodna

2025

Abstract

Sea traffic is fundamental information that needs to be considered while planning vessel operations to enhance navigational safety and operational efficiency. Therefore, several environmental constraints, such as weather and traffic conditions, must be taken into account to minimize delays caused by vessel traffic and improve safety by decreasing collision risks. In this paper, we address the vessel traffic prediction problem, which tackles predicting vessel traffic for ships using several sources of information. We propose a vessel traffic prediction method that processes information obtained from different sources indicating historical traffic and wave conditions for vessels. The proposed method consists of three models processing different types of features and fuses the outputs of these models for the vessel traffic prediction problem. We evaluate the proposed method on real-world historical vessel trajectories and report its performance by providing a comparison with other baselines. The experimental results indicate that our proposed method provides promising results for predicting vessel traffic with a mean squared error of 0.325.

Paper Citation

in Harvard Style

Altan D., Marijan D. and Kholodna T. (2025). Deep Learning-Based Vessel Traffic Prediction Using Historical Density and Wave Features. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1054-1061. DOI: 10.5220/0013258100003890

in Bibtex Style

@conference{icaart25,
author={Dogan Altan and Dusica Marijan and Tetyana Kholodna},
title={Deep Learning-Based Vessel Traffic Prediction Using Historical Density and Wave Features},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1054-1061},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013258100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Deep Learning-Based Vessel Traffic Prediction Using Historical Density and Wave Features
SN - 978-989-758-737-5
AU - Altan D.
AU - Marijan D.
AU - Kholodna T.
PY - 2025
SP - 1054
EP - 1061
DO - 10.5220/0013258100003890
PB - SciTePress