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.
%0 Journal Article
%1 9d36f801cc914168a7b10ed83f31d842
%A Paulig, Niklas
%A Okhrin, Ostap
%D 2024
%I Elsevier Science B.V.
%J Ocean engineering
%K topic_engineering AIS, Big Data-driven, FIS_scads Open-source, Trajectory data, extraction
%R 10.1016/j.oceaneng.2024.119092
%T An open-source framework for data-driven trajectory extraction from AIS data—The α-method
%V 312
%X 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.
@article{9d36f801cc914168a7b10ed83f31d842,
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, 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.},
added-at = {2024-11-28T16:27:18.000+0100},
author = {Paulig, Niklas and Okhrin, Ostap},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/27cfe227373022734f71c864bbc3f042c/scadsfct},
day = 15,
doi = {10.1016/j.oceaneng.2024.119092},
interhash = {51593765bb8e381ae50106c6c8724ba6},
intrahash = {7cfe227373022734f71c864bbc3f042c},
issn = {0029-8018},
journal = {Ocean engineering},
keywords = {topic_engineering AIS, Big Data-driven, FIS_scads Open-source, Trajectory data, extraction},
language = {English},
month = nov,
note = {Publisher Copyright: {\textcopyright} 2024 The Author(s)},
publisher = {Elsevier Science B.V.},
timestamp = {2024-11-28T17:41:00.000+0100},
title = {An open-source framework for data-driven trajectory extraction from AIS data—The α-method},
volume = 312,
year = 2024
}
