全球AIS轨迹中船舶目的地估算方法

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，并创新性地设计了WAY深度学习架构来处理重构轨迹，实现提前数天至数周的长期目的地预测。WAY架构包含轨迹表征层和通道聚合序列处理（CASP）模块：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特征聚合与序列信息传递。此外，我们提出专用于本任务的梯度丢弃（GD）技术，通过基于样本长度随机阻断梯度流，在单标签训练中实现多对多映射，避免偏差反馈激增。基于五年AIS数据的实验表明，WAY在不同轨迹进度下均优于传统空间网格方法；结果同时验证GD技术能带来性能提升。最后，我们通过ETA估计的多任务学习探索了WAY在现实场景中的应用潜力。

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轨迹中船舶目的地估算方法

WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

摘要

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