Dynamically optimized departure and arrival funnels considering aircraft noise and weather conditions

Project Information

© DFG

• Funding Agency: German Research Foundation
• Project Duration: November 1, 2025 – October 31, 2028 (36 months)

For competitive and environmental reasons, the aviation industry must continuously reduce operating costs and minimize delays and emissions. Multi-criteria flight path optimization therefore plays a key role in ATM research. These optimization objectives compete with one another; their interactions must be understood, and their costs must be quantifiable. For the en-route phase, this has already been incorporated into daily operations, for example, with the introduction of Free Route Airspace or weather-dependent, optimal routes in the North Atlantic. However, during approaches to and departures from major airports, high traffic volumes and airspace restrictions often force aircraft to fly at inefficient altitudes and take detours.

Furthermore, aircraft noise at altitudes below 5,000 ft is a dominant factor in public acceptance, and minimizing it is therefore also essential. In this project, so-called Traffic Flow Funnels are being developed. The funnels are designed as procedural spaces that extend current approach and departure routes into a 3D space. Within the funnel, the aircraft flies its optimal altitude profile and flight path. This allows for the current weather conditions and the preferences of airspace users to be taken into account to a significantly greater extent than before. At the same time, the funnels also limit freedom of movement in order to maintain predictability for air traffic control and thus ensure safe and efficient operations.

Current legally mandated methods for noise calculation do not yet allow for the integration of trajectory optimization, primarily because weather conditions (wind, temperature, relative humidity) are not taken into account. To address this, various machine learning approaches are being applied to a large dataset of noise measurements and weather data to predict noise under given conditions.

Publications

• Norman Peter, Thomas Zeh, Hartmut Fricke, (2026): Overcoming limitations of analytical aircraft noise emission estimation using machine learning, International Air Transportation Research & Development Symposium (ATRDS 2026), Delft, NL

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© Sven Ellger

Research Associate

Name

Dipl.-Ing. Norman Peter

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Organization Name

Chair of Air Transport Technology and Logistics

Chair of Air Transport Technology and Logistics

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