An open-source framework for data-driven trajectory extraction from AIS data—The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si191.svg" display="inline" id="d1e4021"><mml:mi>α</mml:mi></mml:math>-method

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, α -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. • Framework for data-driven AIS message decoding, trajectory extraction, and visualization • Maneuverability-dependent quantile-based filtering algorithm for robust trajectory extraction. • Simple and effective methods for trajectory assessment • Open-source implementation of the framework in the Python programming language.