An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting

Abstract

Forecasting vessel locations is of major importance in the maritime domain, with applications in safety, logistics, etc. Nowadays, vessel tracking has become possible largely due to the increased GPS-based data availability. This paper introduces a novel Vessel Location Forecasting (VLF) framework, based on Long-Short Term Memory (LSTM) Neural Networks, aiming to perform effective location forecasting in time horizons up to 60 minutes, even for vessels not recorded in the past. The proposed VLF framework is specially designed for handling vessel data by addressing some major GPS-related obstacles including variable sampling rate, sparse trajectories, and noise contained in such data. Our framework also learns by incorporating a novel trajectory data augmentation method to improve its predictive power. We validate VLF framework using three real-word datasets of vessels moving in different sea areas, comparing with various methods, and examining several aspects. Results prove VLF framework’s generic nature, robustness regarding parameter changes, and superiority against state of the art in terms of prediction accuracy (higher than 30%) and computational effort.