FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
This addresses the need for real-time maritime vessel tracking for applications like collision avoidance and route optimization, representing a strong incremental improvement in speed.
The paper tackles the problem of predicting future vessel locations in maritime environments by introducing FLP-XR, a model that achieves precise predictions while being 2-3 orders of magnitude faster in training and inference than current state-of-the-art methods.
Movements of maritime vessels are inherently complex and challenging to model due to the dynamic and often unpredictable nature of maritime operations. Even within structured maritime environments, such as shipping lanes and port approaches, where vessels adhere to navigational rules and predefined sea routes, uncovering underlying patterns is far from trivial. The necessity for accurate modeling of the mobility of maritime vessels arises from the numerous applications it serves, including risk assessment for collision avoidance, optimization of shipping routes, and efficient port management. This paper introduces FLP-XR, a model that leverages maritime mobility data to construct a robust framework that offers precise predictions while ensuring extremely fast training and inference capabilities. We demonstrate the efficiency of our approach through an extensive experimental study using three real-world AIS datasets. According to the experimental results, FLP-XR outperforms the current state-of-the-art in many cases, whereas it performs 2-3 orders of magnitude faster in terms of training and inference.