Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
This addresses the problem of maritime traffic management for shipping operators, but it is incremental as it applies existing ensemble and neural network methods to a specific challenge.
The paper tackled real-time destination and ETA prediction for maritime traffic using geo-spatial data, achieving 97% accuracy for destination classification and 90% accuracy for ETA prediction.
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.