Abstract
80% of the world trade is carried out through the sea, which shows the importance of maintaining transportation efficiency in the maritime industry. The vessel arrival time and berth allocation pose a significant challenge in the marine field's day-to-day operation, especially when a high number of vessels are waiting at the anchorage to deliver the goods on time. This increases the pressure on the responsible stakeholders and good owners as some cargo must be delivered urgently. Artificial intelligence and machine learning play a vital role in improving the operation of different fields; utilizing such technologies in the maritime industry will facilitate operations and increase efficiency. In this work, the vessel arrival prediction was tackled through implementing different machine-learning models. The historical data was collected from The Norwegian Base Station and Satellites between August 1 and September 24, 2024. Different preprocessing techniques were utilized to clean the dataset and prepare it for modeling. The three models built are Gradient Boosting Regression, K-Nearest Neighbors (KNN) Regression, and Random Forest Regression. Random Forest Regression showed better results than the other two models with R2 value equal to 0.704 and MAPE of 0.0285%.
Library of Congress Subject Headings
Shipping--Data processing; Harbors--Traffic control--Data processing; Machine learning
Publication Date
1-3-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Sanjay Modak
Advisor/Committee Member
Ioannis Karamitsos
Recommended Citation
Albloushi, Anfal Mansour, "Prediction of Vessel Arrival Time to Optimize Berth Allocation in Ports Using Machine Learning Methods" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12022
Campus
RIT Dubai
Plan Codes
PROFST-MS
