PHYSICS-GUIDED MACHINE LEARNING APPLICATIONS FOR AIR TRAFFIC CONTROL
Creators: name: Choi, Hong-Cheol
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 400199
subject: Aerospace engineering not elsewhere classified

description: The Air Traffic Management (ATM) system encompasses complex and safety-critical operations which are mainly managed by Air Traffic Controllers (ATCs) and pilots to ensure safety and efficiency. This air traffic operation becomes more complex and challenging as demands continue to increase. Indeed, the demand for air transport is expected to increase by an average of 4.3% annually over the next 20 years, and the projected number of flights is expected to reach around 90 million by 2040 [1]. This continuous growth of demands can lead to an excessive workload for both ATCs and pilots, thereby resulting in the degradation of the ATM system. To effectively respond to this problem, a lot of effort has been put into developing decision support tools. This dissertation explores and focuses on the development of algorithms for decision support tools in air traffic control, emphasizing specific desirable properties essential for tasks such as tracking the position of aircraft and monitoring air traffic. The primary focus of this dissertation is to combine a data-driven model and a physics-based model systematically, thereby addressing the limitations of previous works in trajectory prediction and anomaly detection. Through a literature review, important properties, including real-time applicability, interpretability, and feasibility, are identified and pursued for practical applications. These properties are integrated into the proposed algorithms which combine data-driven and physics-based models to address dynamic air traffic scenarios effectively. To meet the requirement of real-time applicability, the algorithms are designed to be computationally efficient and adaptable to continuously changing conditions, ensuring timely provision of immediate information and near-instantaneous responses to assist ATCs. Subsequently, interpretability allows controllers to understand the reasoning behind the algorithm’s predictions. This is facilitated by the use of attention mechanisms and explicit physics-based guidance, making the predictions more intuitive and understandable. In addition, anomaly detection algorithms provide human-readable decision boundaries for flight states for a clear understanding. Lastly, feasibility ensures that the algorithms generate realistic aircraft trajectory predictions based on current flight states and air traffic conditions. This is achieved by physics-guided machine learning which leverages both data-driven and physics-based approaches, accounting for the aircraft dynamics and uncertainties. Moreover, practical and operational considerations are integrated into algorithms for real-world applications. This includes developing anomaly detection models that are adaptable to dynamic trajectory patterns to address the complexities of flexible area navigation airspace. Additionally, to reduce the workload of ATCs, providing immediate advisories for anomaly resolution and arrival sequencing is targeted by learning from historical data. By considering these properties with practical considerations, the dissertation presents a suite of algorithms that can effectively support human operators for air traffic control.