An open-source framework for data-driven trajectory extraction from AIS data -- the $\alpha$-method

TL;DR

This paper introduces an open-source, data-driven framework called the $oldsymbol{ m oldsymbol{ extalpha}}$-method for improving the quality of ship trajectories derived from AIS data by filtering and constructing more accurate, long, and continuous trajectories.

Contribution

The paper presents a novel, adaptable, maneuverability-dependent $oldsymbol{ m oldsymbol{ extalpha}}$-quantile-based framework for AIS data processing, enhancing trajectory extraction accuracy.

Findings

Robust extraction of clean, long trajectories from raw AIS data.

Improved data quality for maritime safety and analysis.

Open-source Python implementation available.

Abstract

Ship trajectories from Automatic Identification System (AIS) messages are important in maritime safety, domain awareness, and algorithmic testing. Although the specifications for transmitting and receiving AIS messages are fixed, it is well known that technical inaccuracies and lacking seafarer compliance lead to severe data quality impairment. This paper proposes an adaptable, data-driven, maneuverability-dependent, $\alpha$-quantile-based framework for decoding, constructing, splitting, and assessing trajectories from raw AIS records to improve transparency in AIS data mining. Results indicate the proposed filtering algorithm robustly extracts clean, long, and uninterrupted trajectories for further processing. An open-source Python implementation of the framework is provided.