To meet the requirements of airport surfaces intelligent management,this paper innovatively in-troduces the node2vec algorithm and ROUGE-N evaluation system from the NLP field into aircraft taxiing trajectory prediction research.By establishing a topological structure map of the airport surface,a new multi-feature embedding method for taxiing trajectories is created and framework is developed for long-term trajectory prediction.Taking Shenzhen Airport as an example,we proposed an attention mechanism integrated Bi-LSTM-A prediction model and compared its performance with traditional models like RNN,LSTM,and Bi-LSTM.In view of multi-feature embedding method,it is demonstrated that the Bi-LSTM-A model surpasses the baseline models by an average of 10.11%in precision,14.76%in recall rate,and 12.78%in F1 score.This indicates that the proposed predictive technique significantly en-hances the accuracy of long-term taxiing trajectory predictions and can effectively estimate the operational status of the airport based on flight schedules,thereby improving the intelligence and efficiency of airport ground operations.
关键词
机场场面/滑行轨迹预测/自然语言处理/特征提取/注意力机制
Key words
airport surface/taxiing trajectory prediction/natural language processing/feature extraction/attention mechanism
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