Project description
The digital transformation of air transport management
Air transport management (ATM) requires the collaboration of airlines, service providers and authorities on the ground to ensure smooth day-to-day operations. Advances in technology and the digitalisation of services mean that big data analytics and new risk assessment methodologies can prove useful. The EU-funded SafeOPS project will explore how these future services can help improve the safety and cost-efficiency of air transport operations. It will focus on the decision making process in go-around scenarios, which is of high safety relevance for both airlines and air navigation service providers in ATM. Overall, the project will promote the modernisation of ATM based on artificial intelligence tools and a special focus on the interactions between humans (controllers).
Objective
Maintaining safety and cost-efficiency of air transport operations while increasing the capacity will push the next generation of ATM systems towards digitalization. In the mid-term, a digitalized system in the human operated ATM environment will be capable of delivering reliable predictive analytics based on automated information processing, providing decision support for human operators. SafeOPS supports these future services by investigating the use of big data analytics together with new risk assessment methodologies.
ANSP and airlines are the relevant stakeholders of the aviation business, forming the SafeOPS consortium. Several research institutes complement the consortium. To ensure the high confidentiality levels of the associated datasets, SafeOPS utilizes DataBeacon, a platform that allows fusing and analyzing confidential aviation data. As an exemplary safety-critical scenario, SafeOPS considers go-arounds that are of high safety relevance for both, airlines and ANSPs. Based on successful unstable approach predictions, developed in the Horizon2020 project SafeClouds.eu SafeOPS will carry out go-around predictions and analyze their impact onto the safety and resilience of ATM in detail.
As recognized by the SESAR Single Programming Document, data-driven and machine learning technologies are a cost-efficient asset to reduce current fragmentation and upgrade inefficient old technologies. In turn, they introduce new challenges for ATM stakeholders, from controllers and their training to regulators and certification agencies. SafeOPS addresses some of these challenges by fostering the ATM modernization based on AI tools with an application on safety and resilience through an exemplary case study. It puts a special focus on the interaction among humans (controllers) and within the socio-technical system under the influence of this breakthrough technology. Therefore, it addresses both key performance areas from the Safety and Resilience ATM Master Plan.
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Funding Scheme
RIA - Research and Innovation actionCoordinator
80333 Muenchen
Germany