# Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

arXiv: https://arxiv.org/abs/1810.05567 · HTML: https://mlsanity.com/paper/1810.05567/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning](https://mlsanity.com/paper/2403.09969/) (2024)
- [Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach](https://mlsanity.com/paper/2205.07945/) (2022)
- [A Retrieval-Enhanced Transformer for Multi-Step Port-of-Call Sequence Prediction in Global Liner Shipping](https://mlsanity.com/paper/2605.15937/) (2026)

## Abstract (arXiv)

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction.