Increasing flight volumes and complex airport operations may affect taxi routing and pose challenges for safe and efficient aircraft ground traffic on movement areas. This can lead to extended taxiing distances affecting the aircraft motion behavior on ground, resulting in altered load dynamics on aircraft structures, notably impacting landing gear components.
Digital twin technology plays a crucial role in monitoring aircraft components, such as stress and fatigue spectra during operations, and predicting future component conditions. For monitoring and predicting loads during ground operations within a digital twin framework, the actual aircraft maneuvering trajectories have to be considered and can be modeled by utilizing different data sources containing information on the aircraft's position. However, these data are often sparse, noisy, or misaligned. Therefore, our methodology demonstrates processing and analyzing of aircraft ground maneuvering trajectories using sparse ADS-B data and geospatial airport data, achieving more realistic trajectories compared to raw ADS-B data through consistent map-matching and filtering methods. These trajectories can enhance motion representation accuracy of modeling approaches for load monitoring within safety-driven digital twin applications.