Development and evaluation of data analytics and machine learning approaches for enhanced urban air mobility operations - Alharbi, Abdulrahman Abdullah (Cranfield University, 2023)
Due to their flexibility and general robustness, unmanned aerial vehicles (UAVs), have increasingly been deployed for diverse applications. These include aerial mapping, surveillance, package delivery, and even agriculture. Increased employment, however, has also entailed new demands for smart, nimble and effective UAV traffic-management systems, particularly in urban areas. If numerous, fully automized UAVs are to be flown frequently, and beyond the visual line of sight (BVLoS), then efficient unmanned traffic management (UTM) is essential, not least as UAV traffic will inevitably become denser. In future, indeed, air-traffic management will also be more complex, and airspace more crowded, as the sheer volume of UAVs continues to rise. Consequently, UTM will require swift, efficient decision-making mechanisms. Important challenges also remain in terms of machine-learning algorithm verification, these stemming primarily from a lack of explicability and transparency. Given that traditional safety mechanisms are unequal to the tasks involved, this has been an inhibiting factor in the integration of UAVs into very low-level (VLL) airspace. This thesis develops a data-analytics framework to analyze simulated historical data and characterize traffic-flow patterns in UTM airspace. The framework enhances risk analysis and improves trajectory planning across various airspace regions. It considers all dynamic parameters, such as extreme weather, emergency services, and dynamic airspace structures. Furthermore, and to meet the critical need for accurate congestion prediction in UAS traffic-flow management (UTFM), this study uses state-of-the-art machine learning techniques to integrate air traffic-flow prediction with the intrinsic complexity metric. In this study, air-traffic congestion analysis and prediction will be addressed via a deep-learning methodology, within a UTM context, across a timeframe of three minutes. The proposed model is distinct from approaches that would focus on the more conventional issues of conflict detection, conflict resolution and trajectory prediction. In addition, this thesis proposes a tailored solution to the needs of demand-and- capacity-management (DCM) services. This solution deploys a transparency- based methodology, with a fusion of both black-box and explainable, white-box models. It generates, therefore, an intelligent system that can be both explicable and reasonably comprehensible. The results show that the advisory system will be able to indicate the most appropriate regions for UAV operations, while increasing UTM airspace availability by more than 23%.
Keywords: Deep Learning; Explainable Artificial Intelligence; Long short-term memory (LSTM) networks; Low-Altitude Airspace Operations; Machine learning; Traffic Flow Management; Trajectory data analytics; Unmanned aerial vehicle (UAVs); Unmanned aircraft traffic management (UTM)