基于混合Transformer模型的飞行轨迹预测OA

Efficient and accurate flight trajectory prediction technology is central to achieving intelligent and information-driv-en air traffic management,significantly enhancing the operational efficiency and safety of air traffic.To address the limitations of tra-ditional trajectory prediction methods in handling high-dimensional data and complex spatiotemporal dependencies,this paper pro-poses a hybrid neural network model that combines self-attention mechanisms,convolutional neural networks(CNN),and a Trans-former structure to improve the accuracy and real-time performance of flight trajectory prediction.The proposed model effectively captures spatiotemporal dependencies in trajectory data.Experimental results indicate that,compared to self-attention CNN-LSTM and traditional CNN-LSTM models,this model achieves notable improvements in prediction accuracy for longitude,latitude,and altitude,particularly exhibiting superior predictive performance at critical trajectory points.