Machine Learning based System for Vessel Turnaround Time Prediction
This addresses port efficiency for maritime logistics, but it is incremental as it applies existing methods to a specific domain with new data.
The paper tackles vessel turnaround time prediction by developing a machine learning system using standardized port call data and external maritime big data, achieving increased accuracy compared to the manual expert-based system in Port of Bordeaux.
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.