Deep learning architecture for UAV traffic-density prediction

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dc.contributor.authorAlharbi, Abdulrahman
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.date.accessioned2023-02-15T11:27:31Z
dc.date.available2023-02-15T11:27:31Z
dc.date.issued2023-01-22
dc.description.abstractThe research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average.en_UK
dc.identifier.citationAlharbi A, Petrunin I, Panagiotakopoulos D. (2023) Deep learning architecture for UAV traffic-density prediction. Drones, Volume 7, Issue 2, January 2023, Article number 78en_UK
dc.identifier.issn2504-446X
dc.identifier.urihttps://doi.org/10.3390/drones7020078
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19195
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomplexity metricsen_UK
dc.subjectlong short-term memory (LSTM) networksen_UK
dc.subjectunmanned aerial vehicles (UAVs)en_UK
dc.subjectunmanned traffic management (UTM)en_UK
dc.titleDeep learning architecture for UAV traffic-density predictionen_UK
dc.typeArticleen_UK

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