Ship-Route Prediction Based on a Long Short-Term Memory Network Using Port-to-Port Trajectory Data

Abstract

In this study, a ship-route prediction model based on a long short-term memory network using port-to-port trajectory data is presented. Data from a traditional automatic identification system—often used for ship-route prediction—are limited by uneven sampling intervals and noise. To address these issues, equal-interval data collected every 10 s from a target ship, which is a liner container vessel, were employed. Our study focuses on predicting the entire trajectory between the Gunsan and Busan ports. The root mean square error (RMSE), mean absolute error (MAE), and average distance d¯ between two trajectories were used as the key evaluation metrics. The analysis yielded excellent predictive performance, with the values RMSE = 0.000999, MAE = 0.000672, and d¯ = 0.101 km. This study thus provides a foundation for predicting complete port-to-port routes and offers practical insights for managing vessel operations. Accurate route prediction contributes to reducing port congestion, improving fuel efficiency, and lowering carbon emissions.