Deep learning has achieved significant improvement in various machine learning tasks including image recognition, speech recognition, machine translation and etc. Inspired by the huge success of the paradigm, there have been lots of tries to apply deep learning algorithms to data analytics problems with big data including traffic flow prediction. However, there has been no attempt to apply the deep learning algorithms to the analysis of air traffic data. This paper investigates the effectiveness of the deep learning models in the air traffic delay prediction tasks. By combining multiple models based on the deep learning paradigm, an accurate and robust prediction model has been built which enables an elaborate analysis of the patterns in air traffic delays. In particular, Recurrent Neural Networks (RNN) has shown its great accuracy in modeling sequential data. Day-to-day sequences of the departure and arrival flight delays of an individual airport have been modeled by the Long Short-Term Memory RNN architecture. It has been shown that the accuracy of RNN improves with deeper architectures. In this study, four different ways of building deep RNN architecture are also discussed. Finally, the accuracy of the proposed prediction model was measured, analyzed and compared with previous prediction methods. It shows best accuracy compared with all other methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A deep learning approach to flight delay prediction


    Beteiligte:
    Kim, Young Jin (Autor:in) / Choi, Sun (Autor:in) / Briceno, Simon (Autor:in) / Mavris, Dimitri (Autor:in)


    Erscheinungsdatum :

    01.09.2016


    Format / Umfang :

    468548 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Flight Arrival Delay Prediction Using Deep Learning

    Sharma, Nishant / Vijayalakshmi, S. | IEEE | 2024


    A Deep Learning Approach for Flight Delay Prediction Through Time-Evolving Graphs

    Cai, Kaiquan / Li, Yue / Fang, Yi-Ping et al. | IEEE | 2022


    Machine Learning Approach for Flight Departure Delay Prediction and Analysis

    Esmaeilzadeh, Ehsan / Mokhtarimousavi, Seyedmirsajad | Transportation Research Record | 2020


    A geographical and operational deep graph convolutional approach for flight delay prediction

    CAI, Kaiquan / LI, Yue / ZHU, Yongwen et al. | Elsevier | 2023

    Freier Zugriff

    Flight Delay Prediction Using Machine Learning Techniques

    Tijil, Yash / Dwivedi, Nripendra / Srivastava, Satyam Kumar et al. | IEEE | 2024