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ORCI

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Optimised Runway Centreline Interception

ORCI will explore innovative AI-based solutions to help increase runway throughput using advanced automation support tools in the TMA domain. Specifically, the objective is to provide key information to Air Traffic Controllers in... ver más

30/11/2026

ISA SOFTWARE FRANC...

819K€

Presupuesto del proyecto: 819K€

Líder del proyecto

ISA SOFTWARE FRANCE No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-06-01

HORIZON-SESAR-2023-DES-ER2-WA2-4: ATM application-oriented research f... ExpectedOutcome:Project results are expected to contribute to the following expected outcomes.

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ISA SOFTWARE FRANCE

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Duración del proyecto: 29 meses Fecha Inicio: 2024-06-01
Fecha Fin: 2026-11-30

Líder del proyecto

ISA SOFTWARE FRANCE No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 819K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto ORCI will explore innovative AI-based solutions to help increase runway throughput using advanced automation support tools in the TMA domain. Specifically, the objective is to provide key information to Air Traffic Controllers in final approach sectors, to support informed decisions on when to issue vectoring instructions to aircraft for optimal spacing between consecutive arrivals during medium, high, very high-density and increasingly complex TMA airspace operations. To achieve this objective, the project will develop an AI model that is trained using radar surveillance data and ATC voice communications between pilots and controllers.During the project, Barcelona and Lisbon approach operations will be assessed. This will include interviews with ATCO experts from the respective ANSP partners, as well as in-depth analysis of local arrival characteristics (e.g. geometries, procedures, etc.). In addition, high amounts of radar surveillance and voice communications data will be collected and processed, to support and guide the training and testing of the AI models.The validation of the AI model will be supported by Human in the loop and Fast Time simulation techniques (using the RAMS Plus tool) to ensure that the performance of the AI model is evaluated in a realistic and controlled environment, and to get some initial human performance and safety related feedback.The successful implementation of the AI model is anticipated to optimize delivery of vectoring instructions, leading to enhanced capacity, efficiency, environmental performance, and overall improvements to arrival air traffic management that are consistent with SESAR performance targets. Additional benefits also extend to optimization of the runway throughput by reducing both ATC workload and the potential for human error. The expected solution could also be extended to incorporate the use of time-based separation for arrivals and digitally shared trajectory information coming from the flight-deck

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