Abstract This paper presents models for flight delay prediction by considering both the local effects and network effects for the individual airport. Following a complex network approach, the authors analyse the local and network effects separately. Results indicate that the long‐term flight delays are mainly caused by network effects, while the short‐term flight delays are strongly associated with local delays. Therefore, the existing factors such as temporal variables, weather condition and seasonal effects are replaced with specific novel factors (e.g. crowdedness degree of airport and air traffic system, demand‐capacity imbalance) for flight delay prediction. More specifically, this paper shows that the model prediction performance for both classification (predict whether the flight is delayed) and regression (predict the delay values) achieves higher accuracy when using the novel factors. Random Forest algorithms were trained and tested on the U.S. domestic flights in July 2018, and the results show that for classification model, the accuracy, precision and recall score reach 96.48%, 94.39% and 90.26% when classifying delays are within 15 min. Similarly, for regression model, 93.92% of the test errors are within 15 min.


    Access

    Download


    Export, share and cite



    Title :

    Generation and prediction of flight delays in air transport


    Contributors:
    Qiang Li (author) / Ranzhe Jing (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Generation and prediction of flight delays in air transport

    Li, Qiang / Jing, Ranzhe | Wiley | 2021

    Free access

    Transport delays associated with NASA Langley Flight Simulation Facility

    Smith, R. Marshall / Chung, Victoria I. / Martinez, Debbie | NTRS | 1995


    Transport Delays Associated with NASA Langley Flight Simulation Facility

    R. M. Smith / V. I. Chung / D. Martinez | NTIS | 1995



    Public Transport Incident Prediction Method by Road Traffic Delays

    Gao, H.Z. / Han, Y. / Zang, L.L. et al. | British Library Conference Proceedings | 2009