WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

TL;DR

This paper introduces WAY, a novel deep learning architecture for long-term vessel destination estimation using AIS data, addressing data irregularities and improving prediction accuracy over traditional methods.

Contribution

The paper presents a new deep learning model, WAY, with a specialized trajectory representation and attention mechanisms, plus a Gradient Dropout technique for better vessel destination prediction.

Findings

WAY outperforms traditional spatial grid-based methods.

Gradient Dropout improves model training stability and accuracy.

Model demonstrates potential for real-world ETA estimation applications.

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.