## 4 Discussion  
This study demonstrated that employing a BiLSTM network to develop a predictive model using operational, trajectory-related, and meteorological data can yield promising outcomes. The BiLSTM network was selected for its effectiveness in predicting \(\delta_{IBT}\), which denotes the discrepancy between the scheduled in-block time (SIBT) and the actual in-block time (AIBT). As previously discussed, the increased error in predicting in-block time delays indicates variability within the training dataset. This suggests that the learning process was constrained by the dataset's nature and volume, preventing further improvement. Inconsistencies in the input data likely impacted the model's training performance, leading to suboptimal predictions of in-block time, reflected by an RMSE of 18.37 min which does not satisfy the standard on-time performance window of \(-\)15 to +15 min. This outcome also points to the model's limited ability to generalize effectively from the training data. Considering the limited accuracy in predicting in-block times, this study redirected its focus to evaluate the model's capability in forecasting landing times. Notably, predictions for \(\delta_{LT}\) showed a significant reduction in uncertainty, reflecting more consistent input data and a stronger training process, as supported by an RMSE of 8.71 min. As illustrated in Figure 6B, the model achieved more accomplishment in predicting \(\delta_{LT}\) compared to \(\delta_{IBT}\). The RMSE of 8.71 min during testing indicates that the model performed effectively in forecasting landing times. The ability of the model to predict \(\delta_{LT}\) with such accuracy has several practical applications. For instance, precise landing time predictions can significantly enhance the efficiency of airport resource management [42]. Ground operations teams can optimize the scheduling of gates, stands, and ground handling services, ensuring that resources are allocated efficiently in real-time. Additionally, accurate \(\delta_{LT}\) predictions provide air traffic  
Figure 7: Feature importance comparison for \(\delta_{IBT}\) (**A**,**C**) and \(\delta_{LT}\) (**B**,**D**) using attention mechanism and perturbation analysis. controllers and airport operations teams with the ability to anticipate runway occupancy and taxiway usage, helping to streamline aircraft movements and reduce congestion during peak traffic periods. Moreover, as many of the flights analyzed in this study pertain to long-haul traffic, the model's success in predicting landing times is particularly valuable. Long-haul flights often have more variability in their final approach phases [43], and this model's ability to provide accurate predictions despite such variability suggests a strong potential for improving tactical decision making in airport operations. By incorporating accurate \(\delta_{LT}\) predictions, ground operations planners can better anticipate aircraft arrivals, minimizing delays in both runway and stand utilization, and facilitating more seamless gate turnaround operations [44]. This observation is both compelling and informative, emphasizing that the shortcomings in predicting in-block times are offset by the improved accuracy in forecasting landing times. The disparity suggests that the reduced predictive performance might be linked to the distinct operational phase associated with each variable. Fundamentally, this difference highlights the critical need to understand the nuances of operational phases and their influence on prediction outcomes. The taxiing phase, when an aircraft moves to its parking position, appears to be a critical stage influencing predictive performance. Gaining insights into the factors affecting this phase could be pivotal in enhancing the model's effectiveness. Incorporating additional variables, such as those impacting taxi-in time, may improve prediction accuracy [45].