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.

翻译：传统空中交通管制将空域划分为特定区域，随着交通量增长形成了扩展瓶颈。如何选择空域划分方式并非易事，因为网格尺寸会影响工作负载、移交频率以及每个扇区内协调机制的运行容量。我们提出了一种三阶段流程，可在保留人类监督的同时实现扇区划分与扇区协调的自动化。首先，一个两阶段XGBoost分类器基于23个与位置无关的交通特征预测最优三维网格配置，在源自联邦航空管理局系统广域信息管理回放的65,000样本数据集上达到91.38%的准确率。其次，采用无领导者的Paxos共识协议使飞行器之间自主协调扇区进入过程，在所有测试配置下保持超过96%的进入成功率且近中碰撞率保持低水平。第三，基于高斯过程代理模型的贝叶斯优化通过50次试验为每个机场优化八个协议参数，揭示每个交通环境都需要本质不同的配置组合。该流程为交通需求超出管制员容量的可扩展自主空域管理提供了切实可行的路径。