An intelligent maritime navigation system is expected to play an important role in the realm of Internet of Vessels (IoV). As a key technology in navigation systems, vessel trajectory prediction technology is critical to the IoV. Automatic identification system (AIS), an automated tracking system, is used extensively for vessel trajectory prediction. However, certain characteristics in the AIS data, such as the large number of anchored trajectories in the area, anomalous sharp turns of some trajectories, and the behavioral differences of vessels in different segments, limit the prediction accuracy. In this study, we propose a novel vessel trajectory prediction model for accurate prediction with the following characteristics: (1) an anchor trajectory elimination algorithm to eliminate anchor trajectories; (2) a statistical trajectory restoration algorithm to repair sharp turning; (3) a two-stage clustering algorithm (D-KMEANS) to distinguish vessel behavior; and (4) a deep bidirectional gate recurrent unit (Stacked-BiGRUs) model to predict vessel trajectory and compare the accuracy of the model before and after improvement. The results show that the mean square error and the mean absolute error of the improved model are reduced by 27% and 46%, respectively. This research shows good potential for maritime navigation early warning and safety.

中文翻译：

---

基于 Stacked-BiGRUs 的改进船舶轨迹预测模型

智能海上导航系统有望在船联网（IoV）领域发挥重要作用。作为导航系统中的一项关键技术，船舶轨迹预测技术对车联网至关重要。自动识别系统 (AIS) 是一种自动跟踪系统，广泛用于船舶轨迹预测。然而，AIS数据中的某些特征，如该区域内锚定轨迹数量多、部分轨迹异常急转弯、不同航段的船舶行为差异等，限制了预测的准确性。在本研究中，我们提出了一种用于准确预测的新型血管轨迹预测模型，具有以下特点：（1）一种锚轨迹消除算法，用于消除锚轨迹；(2) 一种用于修复急转弯的统计轨迹恢复算法；(3) 区分血管行为的两阶段聚类算法 (D-KMEANS)；(4) 深度双向门循环单元 (Stacked-BiGRUs) 模型来预测血管轨迹并比较模型改进前后的准确性。结果表明，改进模型的均方误差和平均绝对误差分别降低了27%和46%。这项研究显示了海上航行预警和安全的良好潜力。结果表明，改进模型的均方误差和平均绝对误差分别降低了27%和46%。这项研究显示了海上航行预警和安全的良好潜力。结果表明，改进模型的均方误差和平均绝对误差分别降低了27%和46%。这项研究显示了海上航行预警和安全的良好潜力。