# Machine Learning based System for Vessel Turnaround Time Prediction

arXiv: https://arxiv.org/abs/2104.14980 · HTML: https://mlsanity.com/paper/2104.14980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)

## Built on by (most cited first, 2 of 2)

- [Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach](https://mlsanity.com/paper/2204.04085/) (2022)
- [Predictive Analysis for Optimizing Port Operations](https://mlsanity.com/paper/2401.14498/) (2024)

## Abstract (arXiv)

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.