Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport
arXiv:2606.11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jackson Atlanta International Airport (KATL) and proposes a two-stage, data-driven decision aid that mirrors controller workflow. Stage I predicts the runway exit selected by an arriving aircraft. Stage II predicts whether, given that exit, the aircraft will cross the active departure runway at a designated point or use the end-around taxiway. Models are trained using ASDE-X surface trajectories, aircr
The increasing performance constraints at high-throughput airport hubs necessitate advanced AI solutions to optimize surface operations, driven by the maturity of machine learning and availability of rich operational data.
This development showcases the practical application of AI in critical infrastructure management, directly impacting efficiency, safety, and capacity utilization at major transportation hubs.
Airport surface operations can now leverage data-driven AI models for more precise, predictive control over aircraft movements, leading to reduced taxi times, fuel consumption, and improved overall flow.
- · Airports (especially high-throughput hubs)
- · Airlines
- · Air Traffic Controllers
- · AI/ML Aviation Solutions Providers
- · Inefficient manual airport operation processes
- · Legacy air traffic management systems
Reduced taxi times and fuel burn for individual aircraft at busy airports.
Increased airport capacity and fewer delays, improving overall air travel efficiency.
Potential for an integrated AI-driven global air traffic management system, optimizing network-wide flows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG