Predictive Sectorization and Bayesian Optimized Consensus for Admission Control in Autonomous Airspace Operations

TL;DR

This paper introduces an automated, scalable airspace sectorization and coordination system using machine learning, consensus protocols, and Bayesian optimization to improve autonomous air traffic management.

Contribution

It presents a novel three-stage pipeline combining traffic prediction, aircraft coordination, and parameter tuning for autonomous airspace operations.

Findings

XGBoost classifier achieves 91.38% accuracy in sectorization prediction.

Leaderless Paxos protocol maintains over 96% entry success rate.

Bayesian Optimization tailors protocol parameters to different traffic environments.

Abstract

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.