Democratizing business intelligence and machine learning for air traffic management safety

Abstract

The ways in which Air Navigation Service Providers (ANSPs) monitor safety performance is strongly influenced by international regulations, standards, and agreements, although each State may also add its own local requirements. Particularly in the case of more mature ANSPs, the regulatory safety performance obligations are merely the tip of the iceberg in the undertaken safety performance activities. Much of the indicators, methods and tools are over and above what is required by regulations, either national or international. In modern settings, the usage of Business Intelligence and Machine Learning solutions can be enumerated under the continuous chasing of strategies to foster ANSPs’ safety intelligence capacities towards higher standards.This manuscript shows the development process of an integrated data-driven framework for self-service BI and ML on safety reporting data for the air traffic management system. The proposed framework firstly focuses on the development process of a BI architecture to extract meaningful knowledge from multiple data sources. Then, it progresses discussing how ML solutions may support gaining a deeper understanding of system’s performance and delineating specific safety recommendations. The explorative application of the proposed framework in multiple European ANSPs provides the basis for sharing lessons learned and outlining a possible path to start democratizing safety intelligence in aviation.