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 ($R^2$).

Air carriers shall not readily relinquish their held flight slots. In cases where the historical flight slot pool cannot be easily altered, a pressing need arises for an allocation method that can efficiently utilize the incremental resources of these time slots. This paper presents an integer planning model to address the efficient allocation of incremental airport time slot resources. The model considers the capacity of key resource nodes and flight waveforms as constraints to maximize the total incremental slots. Moreover, it considers the adaptation of strategic and tactical optimization. After conducting a case study using Beijing Capital International Airport for verification, the proposed model effectively reduces potential operational delays by 66.27% while adding 366 to 397-time slots. Notably, the model demonstrates remarkable delay reduction capabilities and can serve as a valuable decision-support tool for the incremental allocation of time slots.