[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128090-en":3,"doc-seo-128090-105":31,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128090,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Development and Evaluation of Data Analytics and Machine Learning Approaches for Enhanced Urban Air Mobility Operations - PhD Thesis","Unmanned aerial vehicles (UAVs) are being deployed across mapping, surveillance, package delivery, and agriculture, but their expansion in urban areas increases the need for smart unmanned traffic management (UTM). Efficient decision-making becomes essential as UAV numbers grow and airspace becomes more crowded. The thesis addresses machine-learning verification challenges caused by limited explicability and transparency, which hinder integration into very low-level airspace. It proposes a data-analytics and deep-learning framework for risk analysis, trajectory planning, and three-minute congestion prediction, integrating air-traffic prediction with an intrinsic complexity metric. A transparency-based demand-and-capacity-management solution combines black-box and explainable white-box models to recommend operational regions and improve airspace availability by over 23%.","ABDULRAHMAN ABDULLAH ALHARBI  \nDEVELOPMENT AND EVALUATION OF DATA ANALYTICS AND MACHINE LEARNING APPROACHES FOR ENHANCED URBAN  \nAIR MOBILITY OPERATIONS  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nDoctor of Philosophy (PhD)  \nAcademic Year: 2019-2023  \nSupervisor: Ivan Petrunin  \nAssociate Supervisor: Dimitrios Panagiotakopoulos  \nDecember 2023  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nDoctor of Philosophy (PhD)  \nAcademic Year 2019-2023  \nABDULRAHMAN ABDULLAH ALHARBI  \nDEVELOPMENT AND EVALUATION OF DATA ANALYTICS AND MACHINE LEARNING APPROACHES FOR ENHANCED URBAN  \nAIR MOBILITY OPERATIONS  \nSupervisor: Ivan Petrunin  \nAssociate Supervisor: Dimitrios Panagiotakopoulos  \nDecember 2023  \nThis thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy  \n© Cranfield University 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nABSTRACT  \nDue 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.  \nImportant 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.  \nThis 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.  \nIn addition, this thesis proposes a tailored solution to the needs of demand-andcapacity-management (DCM) services. This solution deploys a transparencybased 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% .  \nKeywords:  \nDeep 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) .  \nACKNOWLEDGEMENTS  \nThis work is dedicated to my family, who offered invaluable suppor","cbCaikEZR8fOeaJK","https://ap.wps.com/l/cbCaikEZR8fOeaJK","pdf",13534075,5,1,287,"English","en",105,"","[{\"question\":\"Why is UTM important for urban UAV operations?\",\"answer\":\"As UAV deployments increase in urban areas, airspace becomes more crowded and traffic management must support efficient decision-making, especially under beyond-visual-line-of-sight and higher traffic density.\"},{\"question\":\"What main challenge related to machine learning does the thesis address?\",\"answer\":\"It targets the verification difficulty stemming from a lack of explicability and transparency in machine-learning algorithms.\"},{\"question\":\"What does the proposed framework do for traffic management?\",\"answer\":\"It analyzes simulated historical data to characterize traffic-flow patterns, enhance risk analysis, improve trajectory planning across regions, and support three-minute congestion prediction using deep learning.\"},{\"question\":\"How does the thesis support demand-and-capacity management (DCM)?\",\"answer\":\"It develops a transparency-based approach that fuses black-box and explainable white-box models to generate an advisory system for selecting suitable UAV operation regions, increasing airspace availability by more than 23%.\"}]","Development and Evaluation of Data Analytics and Machine Learning Approaches for Enhanced Urban Air Mobility Operations - 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