Machine Learning Model Predicts Aircraft Runway Exits and Taxiway Routes at Atlanta's Hartsfield-Jackson Airport
Researchers have developed a two-stage machine learning framework that predicts which runway exit and taxiway route arriving aircraft will use at Hartsfield-Jackson Atlanta International Airport. The system trains on surface trajectory data, aircraft characteristics, traffic rates, and weather, benchmarking nine classifiers including XGBoost and LightGBM. The tool aims to improve air traffic controller situational awareness and reduce surface congestion at one of the world's busiest airports.
A study posted to arXiv proposes a data-driven decision aid for airport surface operations at Hartsfield-Jackson Atlanta International Airport (KATL), one of the highest-throughput hubs in the world. The two-stage framework first predicts which runway exit an arriving aircraft will take (Stage I), then predicts whether the aircraft will cross an active departure runway or use an end-around taxiway (Stage II). Models were trained on ASDE-X surface surveillance trajectories combined with aircraft type, ramp destination, short-horizon traffic counts, and weather data. Among nine classifiers tested, XGBoost and LightGBM outperformed Random Forest, with Stage I reaching 0.86–0.89 accuracy and Stage II reaching 0.70–0.74 accuracy. Feature importance analysis identified approach speed as the primary driver of exit selection, while departure rate, crossing rate, and ramp destination were the strongest predictors of taxiway routing. The authors acknowledge that minority exit classes remain difficult to predict due to feature-space overlap, as visualized through t-SNE and UMAP analyses, and emphasize that the system is intended to support—not replace—human controller decision-making.
What's missing
The study is a single-airport case study at KATL, and it is unclear how well the framework would generalize to airports with different runway configurations, traffic mixes, or surface layouts. The paper does not report operational testing or real-time deployment results, leaving open questions about latency, integration with existing controller tools, and human-factors acceptance. The low macro-F1 scores for minority classes suggest meaningful prediction gaps that could affect operational reliability, but the practical safety implications of mispredictions are not fully analyzed.
What different sources said
- arXiv cs.LGCenter
Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport
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