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From Prediction to Decision Support - Strengthening Safe and Scalable ATM Services through Automated Risk Analytics based on Operational Data from Aviation Stakeholders

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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Topic(s)

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Funding Scheme

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RIA - Research and Innovation action

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Call for proposal

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(opens in new window) H2020-SESAR-2019-2

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Coordinator

TECHNISCHE UNIVERSITAET MUENCHEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 228 000,00
Address
Arcisstrasse 21
80333 Muenchen
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 228 000,00

Participants (5)

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