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

TL;DR

This paper presents a real-time maritime destination and ETA prediction system using ensemble learning and neural networks, achieving high accuracy in a streaming data challenge.

Contribution

It introduces a novel combination of ensemble methods and neural networks for accurate real-time maritime predictions in streaming data environments.

Findings

97% accuracy in port destination classification

90% ETA prediction accuracy in minutes

Effective use of ensemble learning and neural networks

Abstract

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.