Optimization-Based Autonomous Air Traffic Control for Airspace Capacity Improvement
Name
Baspinar-etal-TAES2020.pdf
Description
Accepted version
Size
1001.22 KB
Format
Adobe PDF
Checksum (MD5)
24d21bf53670897a47eaafe8cf6d7b47
Author(s)
Baspinar, Baris
Koyuncu, Emre
Date Issued
December 2020
Journal
IEEE Transactions on Aerospace and Electronic Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bas ̧pın, Barıs ̧ et al. “Optimization-Based Autonomous Air Traffic Control for Airspace Capacity Improvement.” IEEE Transactions on Aerospace and Electronic Systems, 56, 6 (December 2020): 4814 - 4830 © 2020 The Author(s)
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TAES.2020.3003106
