## 3 Results  
### Prediction Performance  
During the training of the predictive model for estimating \(\delta_{IBT}\) and \(\delta_{LT}\), performance characteristics were iteratively collected for 60 models. The average values of prediction performance indicators for models trained for \(\delta_{IBT}\) were at the level of \(RMSE=23.7736\pm 20.4742\) min, \(MAE=11.2068\pm 1.9567\) min, \(rMean=-1.2613\pm 3.0895\) min, \(rSTD=23.5353\pm 20.5743\) min, and \(R^{2}=0.8050\pm 0.2058\). The resulting models exhibit a relatively high degree of variability, which is caused by the random selection of the training dataset. This implies that to optimize the training process for more stable results in the context of resulting models, additional information should be added to the dataset. Nevertheless, any model trained on the data should achieve predictive performance within the intervals illustrated above. On the other hand, for models predicting \(\delta_{LT}\), the following metrics were obtained: \(RMSE=9.2779\pm 1.4897\) min, \(MAE=6.1911\pm 0.5155\) min, \(rMean=0.3517\pm 1.4294\) min, \(rSTD=9.1748\pm 1.5244\) min, and \(R^{2}=0.8999\pm 0.0243\). Compared to the previous models, the performance in predicting \(\delta_{LT}\) is much more stable, indicating the independence of the random selection of the training dataset on the resulting model. In the context mentioned above, it is also possible to observe that the final models for predicting \(\delta_{IBT}\) and \(\delta_{LT}\) (trained independently of the previous 60 cases) exhibit similar performance to the respective average performance. Figure 5 offers a graphical depiction of the final model training process throughout individual iterations, taking into account RMSE and loss for both training and validation data. Here, the loss signifies the model's performance, quantifying the disparity between predicted and actual values within a specified input dataset. RMSE, on the other hand, gauges the average magnitude of errors in predicted versus actual values, calculated as the square root of the mean squared differences. Before training, the training option was configured to create a model based on the minimum validation loss. In the case of predicting \(\delta_{IBT}\) (Figure 5A), this model was selected with an RMSE of 31.5 min and a loss of 213. This result suggests relatively high error, which may be caused due variations within the training dataset. In this case, it is apparent that the learning process was unable to advance beyond this level. On the contrary, when re-directing our focus on the prediction of \(\delta_{LT}\) and as seen in Figure 5B, the uncertainty seems to significantly decrease, suggesting more stable inputs. The final model for predicting \(\delta_{LT}\) was selected based on the best validation loss, with an  
Figure 5: Visualization of the training process for the model predicting \(\delta_{IBT}\) (**A**) and \(\delta_{LT}\) (**B**). RMSE of 12.1 min and a loss of 20.9. The training and validation loss both decrease and become relatively stabilized at a certain point, which may indicate a good fit of the model. Once the training was successfully performed, the testing of the final models took place, with the results captured in Figure 6. When looking at Figure 6A, the outputs of the testing suggest that the predictability of in-block deviations may operate with a certain volatility. This may be caused due to the unavailability or limited visibility the model has in relation to key factors influencing the time aircraft spends between landing and parking at the stand. In spite of these statements, the RMSE of 18.37 min and Residual Standard Deviation (rSTD) of 18.40 min may still be operationally seen as beneficial when pre-tactical and close-to-real-time efforts to optimize airport resources are made. Furthermore, when looking at Figure 6B, the model performs evidently well when predicting \(\delta_{LT}\) with RMSE at 8.71 min.