AICHAIN looks for enhancing ATM systems by articulating an advanced privacy-preserving federated learning architecture in which neither the training data nor the training model need to be exposed. This will be possible thanks to the combination of two emerging technologies: FedML and Blockchain technologies.

ITACA seeks to accelerate the development, adoption and deployment of new technologies in ATM. It will develop a new set of methodologies and tools enabling a comprehensive assessment of policies and regulations aimed at amplifying the uptake of new technologies within ATM.

BigData4ATM is a research project within SESAR 2020 Exploratory Research which aims to investigate how new sources of passenger-centric data coming from smart personal devices can be analysed to extract relevant information about passengers’ behaviour and how this information can be used to inform ATM decision making processes.

INTUIT explored the potential of a variety of visual analytics and machine learning techniques to improve our understanding of the trade-offs between Air Traffic Management KPAs, identify cause-effect relationships between performance drivers and performance indicators at different scales, and develop new decision support tools for ATM performance monitoring and management.