Researchers have developed a two-stage AI system to predict aircraft taxi-in decisions at Hartsfield-Jackson Atlanta International Airport. The system uses machine learning models, including XGBoost and LightGBM, to forecast which runway exit an aircraft will use and whether it will cross an active departure runway. Trained on ASDE-X surface trajectory data, aircraft characteristics, and weather, the models achieve accuracies between 0.70-0.89 depending on the stage. The research aims to enhance air traffic controller situational awareness by providing calibrated, explainable predictions. AI
IMPACT Enhances air traffic control efficiency and safety through predictive analytics for aircraft movements.
RANK_REASON The cluster contains a research paper detailing a novel application of machine learning models for a specific prediction task.
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