Generation and prediction of flight delays in air transport
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12057 |
| Summary: | 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. |
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| ISSN: | 1751-956X 1751-9578 |