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Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports

Authors: Buselli, Irene; Oneto, Luca; Dambra, Carlo; Verdonk Gallego, Christian; García Martínez, Miguel; Smoker, Anthony; Ike, Nnenna; +2 Authors

Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports

Abstract

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.

Countries
Italy, Sweden
Subjects by Vocabulary

Microsoft Academic Graph classification: Topic model Exploit Computer science System safety computer.software_genre Field (computer science) Taxonomy (general) Cluster analysis business.industry Air traffic management Exploratory data analysis Artificial intelligence business computer Natural language processing

Keywords

TOKAI, Transport Systems and Logistics, Natural Language Processing, General Language Studies and Linguistics, Losses of Separation, Multidisciplinary, Resilience, Safety Reports, Articles, ATM, Safety, Research Article

1. Olsen NS, Shorrock ST: Evaluation of the HFACS-ADF safety classification system: inter-coder consensus and intra-coder consistency.Accid Anal Prev. 2010; 42 (2): 437-44 PubMed Abstract | Publisher Full Text

2. Wrigstad J, Bergström J, Gustafson P: One event, three investigations: The reproduction of a safety norm. Safety Science. 2017; 96: 75-83 Publisher Full Text [OpenAIRE]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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