Optimization of the effect of air traffic control separation methods on aircraft emissions

Air traffic controllers working in area control centers use one or a combination of speed, altitude, and heading maneuvers to prevent conflicts between aircraft with intersecting flight paths. The selection of these methods is crucial for efficient air traffic management; however, it may be constrained by factors such as time pressure, fatigue, and stress. To address these challenges, this thesis presents a mathematical model and a genetic algorithm-based approach to minimize the impact of conflict resolution techniques on aircraft fuel consumption and NOx emissions. Regression models constructed using real flight data are incorporated into the mathematical model as constraints for estimating aircraft fuel consumption and NOx emissions. This study distinguishes itself from other approaches in the literature by allowing the use of any of the three conflict resolution methods. By integrating these components, this thesis presents a more realistic and comprehensive methodology. In conclusion, for a scenario involving two aircraft, the genetic algorithm can find the optimal solution identified by the LINDO solver. As the number of aircraft increases, the solution time of the LINDO solver increases exponentially, whereas the genetic algorithm generates better results in much more reasonable time frames. Additionally, it was observed that the conflict resolution strategies that minimize fuel consumption and NOx emissions are similar. This thesis has significant potential to enhance flight safety and reduce environmental impact by enabling air traffic controllers to manage conflicts more efficiently.