Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation and also produce insights into how and why those implementation decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs, which are both rare events. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation behavior. Notably, characteristics of both types of model suggest that GDP implementation decisions are made primarily based on conditions now or conditions anticipated in the next couple of hours.