实时预测海量船舶轨迹，速度比现有方法快100-1000倍。
FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
- 基于AIS数据构建高速预测框架，支持实时处理。
- 在三个真实数据集上精度超越当前最优模型。
- 训练与推理速度提升2-3个数量级，适合大规模应用。
船舶移动具有高度动态性和不确定性，即使在航线明确的海域，其行为模式也难以建模。准确预测船舶轨迹对碰撞风险评估、航线优化和港口管理至关重要。本文提出FLP-XR模型，利用真实AIS数据构建高效预测框架，在保证高精度的同时实现极快的训练与推理速度。通过在三个真实世界AIS数据集上的大量实验验证，FLP-XR在多数情况下超越当前最先进方法，且训练与推理速度提升2-3个数量级。
原文摘要 · Abstract (English)
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.
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