Optimizing flight routes individually using machine learning.

Individualized flight paths can help limit the man-made global rise in temperature. To achieve this goal, air traffic management must become much more automated.

Today’s airspace structures and air traffic management are primarily optimized for safety and capacity. With the right automation, however, automation can be extended to include climate-friendly targets such as minimal CO₂ emissions and reduced contrail formation.

In the project “Individual and Automated Air Traffic” (DIAL), DLR researchers are developing procedures that enable simultaneous optimization according to several climate-relevant factors and increased capacity in air traffic management. In DIAL, novel meteorological techniques involving machine-learning algorithms inform flight planning, allowing each individual route to be optimized and adapted to the weather in order to ensure safety and climate protection in equal measure.

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