Trajectory clustering for SVR-based Time of Arrival estimation

X Xu, C Liu, J Li, Y Miao - Ocean Engineering, 2022 - Elsevier
Abstract Accurate vessel Time of Arrival (ToA) estimation is important for port operation and
resource management. In this paper, we propose a data-driven approach for estimating the …

Evaluation and prediction of punctuality of vessel arrival at port: a case study of Hong Kong

Z Chu, R Yan, S Wang - Maritime Policy & Management, 2024 - Taylor & Francis
The punctuality of vessel arrival at port is a crucial issue in contemporary port operations.
Uncertainties in vessel arrival can lead to port handling inefficiency and result in economic …

Vessel Trajectory Data Mining: a review

A Troupiotis-Kapeliaris, C Kastrisios, D Zissis - IEEE Access, 2025 - ieeexplore.ieee.org
Recent advancements in sensor and tracking technologies have facilitated the real-time
tracking of marine vessels as they traverse the oceans. As a result, there is an increasing …

Long-term trajectory prediction for oil tankers via grid-based clustering

X Xu, C Liu, J Li, Y Miao, L Zhao - Journal of Marine Science and …, 2023 - mdpi.com
Vessel trajectory prediction is an important step in route planning, which could help improve
the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory …

[HTML][HTML] Enhancing vessel arrival time prediction: A fusion-based deep learning approach

A Abdi, C Amrit - Expert Systems with Applications, 2024 - Elsevier
The logistic community of shippers has struggled to predict the precise arrival time of the
seagoing vessels with reliable certainty. While deep-learning approaches are promising, the …

Towards just-in-time arrival for container ships by the integration of prediction models

J Yu, S Voß - 2023 - scholarspace.manoa.hawaii.edu
Within the context of green shipping, the concept of Just-In-Time (JIT) arrival has attracted
much attention. Research achieves the JIT arrival for container ships by combining the berth …

Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning

X Zhang, X Fu, Z Xiao, H Xu, X Wei, J Koh… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-
data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted …

Predicting vessel arrival times on inland waterways: A tree-based stacking approach

J Lei, Z Chu, Y Wu, X Liu, M Luo, W He, C Liu - Ocean Engineering, 2024 - Elsevier
Estimating vessel arrival times is crucial for maintaining efficient transportation operations
and ensuring the stability of the entire supply chain in maritime transportation. Vessel arrival …

A Neural Network Approach for ETA Prediction in Inland Waterway Transport

P Wenzel, R Jovanovic, F Schulte - International Conference on …, 2023 - Springer
Ensuring the accuracy of the estimated time of arrival (ETA) information for ships
approaching ports and inland terminals is increasingly critical today. Waterway …

Mobility Data Mining: the Maritime Use Case

A Troupiotis-Kapeliaris, D Zissis… - … on Metrology for …, 2024 - ieeexplore.ieee.org
Maritime mobility monitoring can be achieved through remote sensing and self-reporting
systems, which produce large datasets enabling the extraction of valuable information on …