Abstract
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.
Abstract (translated)
自动识别系统（AIS）能够实现基于数据的海上监视，但面临着可靠性问题和时间间隔不规则的问题。我们提出了一种差异化的方法来解决利用全球范围内的AIS数据进行船舶目的地预测时所面临的问题：将长距离港口间的轨迹重新定义为嵌套序列结构。通过使用空间网格，这种方法可以减轻时空偏差并保持详细的分辨率。我们引入了一个新颖的深度学习架构——WAY（Way Ahead），专门设计用于处理这些重构后的轨迹，以实现数天乃至数周前的长期目的地预测。WAY包括一个轨迹表示层和通道聚合顺序处理(CASP)模块。 该表示层从运动学特征和非运动学特征中生成多通道向量序列。CASP块利用多头通道注意和自注意力机制来进行聚合及顺序信息传递。此外，我们还提出了一种任务特定的梯度丢弃（GD）技术，以实现在单一标签上的许多到许多的训练，通过随机阻断基于样本长度的梯度流来防止偏差反馈激增。 在5年的AIS数据集上进行的实验表明，WAY优于传统的基于空间网格的方法，无论是在轨迹进程方面。结果进一步证实了采用GD技术能够带来性能提升。最后，我们通过ETA估计的多任务学习探索了WAY在现实世界应用中的潜力。
URL
https://arxiv.org/abs/2512.13190
