Description du projet
La transformation numérique de la gestion du transport aérien
La gestion du transport aérien (ATM, pour air transport management) requiert la collaboration des compagnies aériennes, des prestataires de services et des autorités au sol pour assurer le bon déroulement des opérations quotidiennes. Les progrès technologiques et la numérisation des services expliquent l’utilité de l’analyse des mégadonnées et des nouvelles méthodes d’évaluation des risques. Le projet SafeOPS, financé par l’UE, étudiera la manière dont ces futurs services peuvent contribuer à améliorer la sécurité et la rentabilité des opérations de transport aérien. Il se concentrera sur le processus de prise de décision dans des scénarios de remise des gaz, qui revêt une énorme importance en matière de sécurité, tant pour les compagnies aériennes que pour les prestataires de services de navigation aérienne dans la gestion du trafic aérien. Dans l’ensemble, le projet encouragera la modernisation de la gestion du trafic aérien en s’appuyant sur des outils d’intelligence artificielle et en mettant l’accent sur les interactions entre humains (contrôleurs).
Objectif
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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RIA - Research and Innovation actionCoordinateur
80333 Muenchen
Allemagne