Abstract
Demand upon the future Air Traffic Management (ATM) systems is expected to grow to possibly exceed available system capacity, pushing forward the need for automation and digitisation to maintain safety while increasing efficiency. This work focuses on a manifestation of ATM safety, the Loss of Separation (LoS), exploiting safety reports and ATM-system data (e.g., flights information, radar tracks, and Air Traffic Control events).
Current research on Data-Driven Models (DDMs) is rarely able to support safety practitioners in the process of investigation of an incident after it happened. Furthermore, integration between different sources of data (i.e., free-text reports and structured ATM data) is almost never exploited.
To fill these gaps, the authors propose (i) to automatically extract information from Safety Reports and (ii) to develop a DDM able to automatically assess if the Pilots or the Air Traffic Controller (ATCo) or both contributed to the incident, as soon as the LoS happens.
The LoSs’ reported in the public database of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo (CEANITA) support the authors’ proposal .
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Acknowledgements
This project has received funding from the SESAR Joint Undertaking (JU) trough EU-H2020-ICT Project FARO - saFety And Resilience guidelines for aviatiOn (G.A. 892542). The dissemination reflects only the authors’ view and the SJU is not responsible for any use that may be made of the information it contains.
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Buselli, I., Oneto, L., Dambra, C., Gallego, C.V., Martinez, M.G. (2023). Data-Driven Methods for Aviation Safety: From Data to Knowledge. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_13
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