FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913885681876992 |
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| author | Theodoropoulos, George S. Patakis, Andreas Tritsarolis, Andreas Theodoridis, Yannis |
| author_facet | Theodoropoulos, George S. Patakis, Andreas Tritsarolis, Andreas Theodoridis, Yannis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time Theodoropoulos, George S. Patakis, Andreas Tritsarolis, Andreas Theodoridis, Yannis Machine Learning Signal Processing 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. |
| title | FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2503.13491 |