Data-Driven Methods for Aviation Safety: From Data to Knowledge

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.