Traffic flow prediction for UTM application: a deep learning approach

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dc.contributor.authorAlharbi, Abdulrahman
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.date.accessioned2022-11-11T13:41:44Z
dc.date.available2022-11-11T13:41:44Z
dc.date.issued2022-10-31
dc.description.abstractOver the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models.en_UK
dc.identifier.citationAlharbi A, Petrunin I, Panagiotakopoulos D. (2022) Traffic flow prediction for UTM application: a deep learning approach. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 18-22 September 2022, Portsmouth, Virginia, USAen_UK
dc.identifier.eisbn978-1-6654-8607-1
dc.identifier.eissn2155-7209
dc.identifier.isbn2155-7195
dc.identifier.isbn978-1-6654-8608-8
dc.identifier.urihttps://doi.org/10.1109/DASC55683.2022.9925774
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18702
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectcomplexity metricsen_UK
dc.subjectlong short-term memory (LSTM) networksen_UK
dc.subjectuncrewed aerial vehicle (UAVs)en_UK
dc.subjectuncrewed aircraft traffic management (UTM)en_UK
dc.titleTraffic flow prediction for UTM application: a deep learning approachen_UK
dc.typeConference paperen_UK

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