Abstract: With the rapid development of global maritime transportation, ship trajectory prediction plays an important role in shipping safety and management. However, achieving high-precision and physically feasible continuous trajectory prediction remains a key challenge due to the large-scale ship trajectory data and the uncertainty of complex maritime environments. Traditional prediction methods have limitations in handling complex maritime environments and large-scale dynamic data. To address these challenges, this paper proposes a geographically constrained multi-method fusion ship trajectory prediction model. The model introduces a geographical constraint loss function to optimize the accuracy, heading stability, and physical feasibility of trajectory predictions. Additionally, a multi-method fusion network structure is designed, incorporating bidirectional gated recurrent units, attention mechanisms, and multi-scale convolutions, which enhances the ability to extract temporal features and integrate multi-scale information. Experimental results demonstrate that the proposed model achieves lower prediction errors across multiple maritime datasets, with particularly significant advantages in long-term predictions compared to existing models. The study confirms that this model offers high accuracy and stability in ship trajectory prediction, providing effective support for practical applications in the maritime field.

摘要： 随着全球海上运输的快速发展，船舶轨迹预测在航运安全与管理中扮演着重要角色。然而，面对大规模船舶轨迹数据及复杂海上环境的不确定性，如何实现高精度且物理合理的连续轨迹预测仍是一个关键挑战。传统预测方法在处理复杂海上环境和大规模动态数据时存在局限。为此，本文提出了一种基于地理约束的多方法融合船舶轨迹预测模型。该模型通过引入地理约束损失函数，优化了轨迹预测中的位置精度、航向稳定性和物理合理性。同时，结合双向门控循环单元、注意力机制和多尺度卷积等模块，设计了多方法融合的船舶轨迹预测网络结构，提升了时序特征提取和多尺度信息融合的能力。实验结果表明，本文模型在多个海域数据集上均表现出较低的预测误差，特别是在长时间预测中相较于现有模型具有显著优势。研究证明该模型在船舶轨迹预测中具有较高的准确性和稳定性，能够为海事领域的实际应用提供有效支持。