Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based
on variables such as traffic and meteorological conditions, making it a daunting task in air
traffic management due to its dependency on volatile operational and environmental factors.
This paper improves upon our previous work [1] on using offline model-free reinforcement
learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel
integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program)
and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its
adaptability to quick wind changes. The performance is evaluated using two major US airports,
Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To
counter scalability issues presented by the addition of discrete forecast variables, we transitioned
to a continuous state space model, ensuring scalability and inclusion of longer forecast data.
The results of our experiments reflect significant improvements in the RCA tool’s prediction
accuracy.