Descripción del proyecto
La transformación digital de la gestión del transporte aéreo
Para garantizar la fluidez de las operaciones diarias es necesario que líneas aéreas, proveedores de servicios y autoridades en tierra colaboren en la gestión del transporte aéreo (GTA). Los avances en tecnología y la digitalización de servicios permiten aprovechar el análisis de macrodatos y las nuevas metodologías de evaluación de riesgos. El proyecto SafeOPS, financiado por la Unión Europea, explorará cómo estos servicios futuros pueden ayudar a mejorar la seguridad y la rentabilidad de las operaciones de transporte aéreo. Para ello, se estudiará el proceso de toma de decisiones en supuestos alternativos de gran relevancia para la seguridad, tanto para las aerolíneas como para los proveedores de servicios de navegación aérea en la GTA. En términos generales, el proyecto impulsará la modernización de la GTA a partir de herramientas de inteligencia artificial, pero sobre todo tendrá en cuenta las interacciones entre personas (controladores).
Objetivo
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 actionCoordinador
80333 Muenchen
Alemania