Flight trajectory prediction is a critical task in aviation, enabling efficient air traffic management and ensuring safe and seamless flight operations. Conventional methods for trajectory prediction struggle to capture complex spatiotemporal dependencies and uncertainties inherent in the aviation domain. Within this study, we introduce an innovative method to predict flight trajectories, named BayesCoordLSTM. It is a hybrid model that transforms coordinate and applies Bayesian theorem into the ConvLSTM models. The proposed model leverages the spatial features gleaned by the Convolutional Neural Network (CNN) architecture and the temporal dependencies captured by Long-Short Term Memory (LSTM) to enhance the accuracy of trajectory predictions. By incorporating Bayesian theorem, our model provides a probabilistic trajectory forecasts and associated confidence levels while coordinate transformation enhances spatial awareness and predictive capabilities. The paper presents experimental results demonstrating the effectiveness of the proposed BayesCoordLSTM-based approach in improving flight trajectory prediction accuracy, with a focus on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values. The integration of the Bayesian theorem and Coordinate transformation into ConvLSTM models represents a substantial advancement in the field of flight trajectory prediction.


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    Titel :

    Improving Flight Trajectory Predictions with Bayesian-Optimized ConvLSTM Models


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Nguyen, Thi Dieu Linh (Herausgeber:in) / Dawson, Maurice (Herausgeber:in) / Ngoc, Le Anh (Herausgeber:in) / Lam, Kwok Yan (Herausgeber:in) / Nghiem, Thi-Lich (Autor:in) / Le, Thi-Lan (Autor:in) / Le, Viet-Duc (Autor:in) / Maréchal, Pierre (Autor:in) / Delahaye, Daniel (Autor:in)

    Kongress:

    The International Conference on Intelligent Systems & Networks ; 2024 ; Hanoi, Vietnam March 21, 2024 - March 22, 2024



    Erscheinungsdatum :

    01.09.2024


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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