Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment

TL;DR

This paper presents a comprehensive framework for modeling historical AIS data to improve vessel path prediction accuracy, addressing challenges of irregular and low-quality data, and demonstrating superior performance over existing methods.

Contribution

The paper introduces a novel comprehensive framework for modeling AIS data for vessel path prediction, outperforming existing approaches.

Findings

Proposed method significantly outperforms baseline models.

Effective handling of irregular and low-quality AIS data.

Enhanced accuracy in vessel trajectory prediction.

Abstract

The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intelligence, Automatic Identification System (AIS) plays an important role because it recently has been made compulsory for large international commercial vessels and is able to provide nearly real-time information of the vessel. Therefore AIS data based vessel path prediction is a promising way in future maritime intelligence. However, real-world AIS data collected online are just highly irregular trajectory segments (AIS message sequences) from different types of vessels and geographical regions, with possibly very low data quality. So even there are some works studying how to build a path prediction model using historical AIS data, but still, it is a very challenging problem. In this paper, we propose a comprehensive framework to model massive historical AIS trajectory segments for accurate vessel path prediction. Experimental comparisons with existing popular methods are made to validate the proposed approach and results show that our approach could outperform the baseline methods by a wide margin.