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 longterm 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.

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

“…A more reasonable optimal hyperplane was obtained by adjusting the sample penalty and the margin between the sample and the hyperplane, which effectively predicted the flight delay grade and reduced the negative impact of flight delay. Li et al [20] proposed a prediction method of flight delay based on considering local and network effects of individual airports, at the same time, replacing existing time variables, weather conditions, seasonal effects and other factors with specific new factors such as the congestion level of air traffic systems, the imbalance of demand capacity and so on for the performance achieved higher accuracy. Wang et al [21] proposed a flight delay classification method based on variational mode decomposition, Markov depth function and K-means data clustering, which improved the accuracy of flight delay classification.…”

Flight delay forecasting and analysis of direct and indirect factors

“…The analysis of causes on the whole network is helpful to gain an overall understanding of critical factors. Qiang uses the Random Forest (RF) algorithm to predict the delay of a single airport [12], and the method was validated by U.S. domestic flights. However, it cannot explain the delay for one flight.…”

“…Combining Equations (9)(10)(11)(12), the stress of the passenger is related to the time of the notification and the accuracy of the expected time. In this paper, Equation ( 9) is used to measure the impact of delay information released by the forecast results in Section 3.1 on passenger stress.…”