Development and Evaluation of a Real-Time Capable Algorithm and an Innovative Representation of Conflict Area Between Two Aircraft

Kurzfassung

Modern air traffic management (ATM) systems face mounting challenges in detecting and resolving potential flight conflicts, driven by the increasing volume of air traffic, shortage of controllers, and the inherent variability of real-world conditions. Factors such as wind, pilot reactions, and aircraft speed deviations introduce uncertainties that compromise the accuracy of trajectory predictions, elevating safety risks in high-density airspace. Traditional conflict detection methods, which rely heavily on deterministic and trajectory-based models, often fail to address these complexities, leading to false alarms or missed conflicts. To address these limitations, this research presents an advanced medium-term conflict detection algorithm that incorporates probabilistic modeling, dynamic geometric representation, and user-centric visualization techniques. The core of the proposed system is a four-dimensional (4D) representation of aircraft conflict zones, encompassing spatial dimensions and time. This area-based conflict detection approach employs ellipsoidal models to define protected zones around aircraft, enabling precise conflict identification by evaluating spatial and temporal overlap rather than just trajectory points. Monte Carlo simulations are used to capture and quantify uncertainties, generating probabilistic conflict assessments. The system enhances air traffic controllers situational awareness with vertical and horizontal conflict views, augmented by intuitive color-coded risk indicators - red for high risk, orange for moderate risk, and green for low risk. Unlike conventional methods that focus on linear trajectories, the area-based approach accounts for the dynamic nature of aircraft movement and uncertainties. By dynamically adjusting ellipsoid dimensions based on factors such as wake turbulence and wind variations, the model adapts to real-time conditions, providing a realistic depiction of potential conflicts. The integration of a user-friendly graphical interface further simplifies decision-making, offering controllers an efficient tool for managing airspace safely and proactively. Testing in simulated air traffic environments demonstrated the algorithm's strong performance in terms of accuracy, scalability, and usability. Participants validated the conceptual soundness of the system by rating it 8 or higher, and the graphical user interface received positive feedback, with 60% acknowledging its clarity and usability. Map visualizations were particularly wellreceived, with a score of 8 or higher on a 10-point scale, with 50% giving a score of 9 and 20% assigning a perfect 10. This strong positive feedback highlights the tool's effectiveness in conveying conflict information clearly and intuitively. The study identifies areas such as pilot response modeling and computational speed for further improvement, while effectively accounting for key uncertainties like wind data, pilot reactions, and speed deviations. This work builds the base for later operational enhancements by improving conflict detection in current air traffic management systems and supporting the development of autonomous solutions. By enhancing situational awareness and addressing operational uncertainties, the system contributes to safer and more efficient airspace management, particularly in dense and complex traffic environments.