Predicting Port Tugboat Operations for Arriving and Departing Vessels Using Machine Learning

Abstract

Today, predicting the number of tugs required to assist in a towing operation many days in advance is difficult. Towing operations, being a complicated process, are prone to human errors and conflicts, which can have severe financial consequences for all parties involved. In this thesis, a method for extracting port tugboat operations for incoming and outgoing vessels is proposed. Using the obtained tugboat operations dataset, a machine learning model is built in order to predict the number of tugs required to assist in a towing operation. The data used is a year of historical Baltic Sea AIS data and weather data from nearby weather stations near the two analysis ports. The recommended ideas and their implementation were a success from a performance standpoint. The proposed method for extracting towing operations detected the vast majority of towing operations within the analysis area. The obtained tugboat operations dataset was then used during the model construction phase. The obtained models are port-specific. One of the models achieved an overall accuracy of 87.0\%, while the other achieved an accuracy of 91.5\%. The results demonstrated that it is possible to develop a viable predictive tool for tugboat operations. When deployed, the proposed method will enable port and tugboat operators to make faster and more efficient decisions, resulting in increased operational efficiency in the port area.