Optimization-Based Autonomous Air Traffic Control for Airspace Capacity Improvement

Abstract

In order to handle increasing demand in air transportation, high-level automation support seems inevitable. This article presents an optimization-based autonomous air traffic control (ATC) system and the determination of airspace capacity with respect to the proposed system. We model aircraft dynamics and guidance procedures for simulation of aircraft motion and trajectory prediction. The predicted trajectories are used during decision process and simulation of aircraft motion is the key factor to create a traffic environment for estimation of airspace capacity. We define the interventions of an air traffic controller (ATCo) as a set of maneuvers that is appropriate for real air traffic operations. The decision process of the designed ATC system is based on integer linear programming (ILP) constructed via a mapping process that contains discretization of the airspace with predicted trajectories to improve the time performance of conflict detection and resolution. We also present a procedure to estimate the airspace capacity with the proposed ATC system. This procedure consists of constructing a stochastic traffic simulation environment that includes the structure of the evaluated airspace. The approach is validated on real air traffic data for enroute airspace, and it is also shown that the designed ATC system can manage traffic much denser than current traffic.