The prediction of airport inbound and outbound traffic is a hot research direction in civil aviation air traffic management. Using the historical data of air traffic as the data source of the traffic prediction model, the traffic data are processed and machine learning algorithm models such as Support Vector Machine (SVM) linear regression, LSTM (Long Short-Term Memory) recurrent neural network, and BP neural network are used to predict the air traffic. The experiments, analysis, and generalization of relevant machine learning algorithms for air traffic flow prediction are conducted. The experiments show that the prediction results are based on historical airspace traffic data, the LSTM model has the highest accuracy, the SVM linear regression has the second-highest prediction effect, and the BP neural network has a poor prediction effect and insufficient stability. The experimental results demonstrate that the LSTM-based inbound and outbound traffic prediction model can achieve airport traffic prediction based on historical traffic data. The usability and accuracy of the LSTM-based prediction results are illustrated by comparing different algorithms, proving that the LSTM model can be used for future urban air traffic flow prediction. It is demonstrated that the LSTM model can be used for future urban air traffic flow prediction. It provides a theoretical and reference basis for the future air traffic flow management of intelligent urban transportation systems.


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    Title :

    LSTM-Based Prediction of Airport Aircraft in and Outflow


    Additional title:

    Lect.Notes Social.Inform.


    Contributors:
    Wang, Xianbin (editor) / Wong, Kai-Kit (editor) / Chen, Shanji (editor) / Liu, Mingqian (editor) / Li, Baoqiang (author) / Huang, Jin (author) / Duan, Yamei (author) / Zhao, Yuan (author)

    Conference:

    International Conference on Artificial Intelligence for Communications and Networks ; 2021 September 18, 2021 - September 19, 2021



    Publication date :

    2021-11-03


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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