Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety and economic benefits of flights. We use the combination of local weather and flight operation data along with the ATM airport performance (ATMAP) algorithm to quantify the weather and to generate an ATMAP score. We then compared the accuracy of three machine learning models, Support Vector Machine, Random Forest, and XGBoost, to estimate the probability of GDPs. The results of the weather analysis, performed by the ATMAP algorithm, indicated that the ceiling was the most critical weather factor. Lastly, we used two linear regression models (ridge and LASSO) and a non-linear regression model (decision tree) to predict departure flight delays during GDP. The predictive accuracy of the regression models was enhanced by an increase in ATMAP scores, with the decision tree model outperforming the other models, resulting in an improvement of 8.8% in its correlation coefficient (R2).

An aircraft four-dimensional(4-D) trajectory prediction method is proposed for the pre-tactical stage. The method is based on point mass model and total energy model to establish aircraft dynamic model and wind correction model, and use the wind forecast data to correct the trajectory, and gradually deduce the 4-D trajectory by computer recursive method. Based on the flight plan and the aircraft performance data in BADA3.11, the 4-D trajectory of the flight can be predicted. Finally, the accuracy of the prediction method was verified for flight KLM888 on the VHHH-EHAM international long route, where the difference between the predicted trajectory flight time and the planned flight time was only 0.73%.

Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to the unpredictability of weather and air traffic, as well as to increase controller efficiency. A K-prototype clustering technique and grey correlation analysis are proposed to discover similar days to address the problem of similar identification. Firstly, the weather and air traffic datasets are used to create a set of features broken down into numerical and categorical attributes. Secondly, the historical data are clustered using the K-prototype clustering, which is then paired with grey correlation analysis to identify days similar to the reference day and examine the traffic management initiatives employed on that day. Finally, the research uses actual weather information and aircraft schedules from Nanjing Lukou International Airport as examples. The outcomes demonstrate that the similar days picked by the model are representative and can serve as a foundation for airport controllers' decision-making.