Machine Learning based System for Vessel Turnaround Time Prediction

TL;DR

This paper introduces a machine learning system for predicting vessel turnaround times using port call data, external maritime data, and extensive evaluation, outperforming manual expert-based methods in accuracy.

Contribution

It presents a novel data-driven system that leverages machine learning and external maritime data to improve vessel turnaround time predictions.

Findings

Increased prediction accuracy over manual systems

Effective use of 11 years of historical port data

Successful validation with live operational data

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 the current manual expert-based system in Port of Bordeaux.