Predictive Sectorization and Bayesian Optimized Consensus for Admission Control in Autonomous Airspace Operations
For air traffic management, this work provides a scalable, autonomous solution to sectorization and coordination, addressing the growing gap between traffic demand and controller capacity.
This paper tackles the scalability bottleneck of conventional airspace sectorization by automating sector design and coordination. The pipeline achieves 91.38% accuracy in predicting optimal grid configurations and maintains over 96% entry success with low collision rates using a leaderless Paxos protocol.
Conventional air traffic control divides airspace into specific regions, creating a scaling bottleneck as traffic grows. Choosing how to partition airspace is not straightforward because grid size affects workload, handoff frequency, and the capacity of whatever coordination mechanism operates within each sector. We present a three stage pipeline that automates sectorization and sector coordination while preserving human oversight. First, a two stage XGBoost classifier predicts the optimal 3D grid configuration from 23 location-agnostic traffic features, achieving 91.38% accuracy on a 65,000 sample dataset derived from Federal Aviation Administration System Wide Information Management replays. Second, a leaderless Paxos consensus protocol lets aircraft coordinate sector entries among themselves, maintaining above 96% entry success with low near mid-air collision rates across all tested configurations. Third, Bayesian Optimization with a Gaussian Process surrogate tunes eight protocol parameters per airport in 50 trials, revealing that each traffic environment requires a qualitatively different configuration. The resulting pipeline offers a practical path toward scalable, autonomous airspace management as traffic demand outpaces controller capacity.