Machine Learning based System for Vessel Turnaround Time Prediction
Dejan Stepec, Tomaz Martincic, Fabrice Klein, Daniel Vladusic, Joao Pita Costa
Abstract
In this paper we present a novel system for predicting vessel turnaround time, based on machine learning and standardized port call data. We also investigate the use of specific external maritime big data, to enhance the accuracy of the available data and improve the performance of the developed system. An extensive evaluation is performed in Port of Bordeaux, where we report the results on 11 years of historical port call data and provide verification on live, operational data from the port. The proposed automated data-driven turnaround time prediction system is able to perform with increased accuracy, in comparison with current manual expert-based system in Port of Bordeaux.