全球AIS轨迹中船舶目的地预测方法

摘要

自动识别系统（AIS）虽能实现数据驱动的海事监控，但存在可靠性不足与数据间隔不规则的问题。针对全球范围AIS数据的船舶目的地估计任务，我们提出一种差异化方法，将长距离港到港轨迹重构为嵌套式序列结构。该方法通过空间网格化在保持精细分辨率的同时缓解时空偏差。我们设计了新颖的深度学习架构WAY，用于处理重构后的轨迹以实现提前数天至数周的长期目的地预测。WAY由轨迹表征层和通道聚合序列处理（CASP）模块构成：表征层从运动学与非运动学特征生成多通道向量序列；CASP模块采用多头通道注意力与自注意力机制实现信息聚合与序列传递。此外，我们提出专用于本任务的梯度丢弃（GD）技术，通过对单标签样本进行多对多训练，基于样本长度随机阻断梯度流以抑制偏差反馈激增。在五年期AIS数据上的实验表明，WAY相较于传统空间网格方法具有显著优势，且不受轨迹进度影响。结果进一步验证GD技术能提升模型性能。最后，我们通过ETA估计的多任务学习探索了WAY在实际应用中的潜力。

English

The Automatic Identification System (AIS) enables data-driven maritime surveillance but suffers from reliability issues and irregular intervals. We address vessel destination estimation using global-scope AIS data by proposing a differentiated approach that recasts long port-to-port trajectories as a nested sequence structure. Using spatial grids, this method mitigates spatio-temporal bias while preserving detailed resolution. We introduce a novel deep learning architecture, WAY, designed to process these reformulated trajectories for long-term destination estimation days to weeks in advance. WAY comprises a trajectory representation layer and Channel-Aggregative Sequential Processing (CASP) blocks. The representation layer generates multi-channel vector sequences from kinematic and non-kinematic features. CASP blocks utilize multi-headed channel- and self-attention for aggregation and sequential information delivery. Additionally, we propose a task-specialized Gradient Dropout (GD) technique to enable many-to-many training on single labels, preventing biased feedback surges by stochastically blocking gradient flow based on sample length. Experiments on 5-year AIS data demonstrate WAY's superiority over conventional spatial grid-based approaches regardless of trajectory progression. Results further confirm that adopting GD leads to performance gains. Finally, we explore WAY's potential for real-world application through multitask learning for ETA estimation.

全球AIS轨迹中船舶目的地预测方法

WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

摘要

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