## 5 Conclusions
Furthermore, if these models were applicable across different airport environments, they could offer greater flexibility for airport operators, reducing their reliance on aircraft position reports from external systems like ETFMS. By improving the predictability of arrival time deviations, these models could also enhance the accuracy of departure scheduling, helping to stabilize target off-block time and target take-off time declarations, which are crucial for managing airport and air traffic flows. In summary, while this study encountered challenges in predicting in-block time deviations, the success in predicting landing times demonstrates the competence of machinelearning models in airport operations. The results provide grounds for further research and development, particularly in improving resource allocation and operational efficiency. Although this work focuses on optimizing operations at the local level, the implications of improved predictability may benefit the wider air traffic management network as well. The outcomes of this study, particularly the demonstrated precision in predicting landing times, offer a promising avenue for enhancing the operational planning and performance of airports. Conceptualization, V.S., M.S. and L.S. ; methodology, V.S. and M.S. ; software, V.S., M.M. and U.A. ; validation, V.S., L.H., M.M. and U.A. ; formal analysis, V.S., M.S., L.H., M.M. and U.A. ; investigation, V.S., M.S. and L.S. ; resources, M.S. ; data curation, V.S. and M.S. ; writing--original draft preparation, V.S., M.S., L.H. and U.A. ; writing--review and editing, V.S. ; visualization, V.S. and L.H. ; supervision, V.S. and L.S. ; project administration, V.S. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. This research was supported by internal resources from the Faculty of Transportation Sciences in Prague, without specific project-based funding. The APC was funded by the internal fund for support of research and development at the Faculty of Transportation Sciences in Prague. The data supporting the reported results of this study are available upon request from the corresponding author. No publicly archived datasets were generated or analyzed during this study. We gratefully acknowledge the support of EUROCONTROL, the Czech Hydrometeorological Institute, Prague International Airport, and Air Navigation Services of the Czech Republic for their provision of essential data and resources that contributed to this study. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ADEP & Airport of Departure \\ ANS CR & Air Navigation Services of the Czech Republic \\ AO & Airport Operators \\ AODB & Airport Operations Database \\ ATCM & Air Traffic Flow and Capacity Management \\ CTFM & Current Tactical Flight Model \\ ETFMS & Enhanced Tactical Flow Management System \\ FFT & Filed Tactical Flight Model \\ CHMU & Czech Hydrometeorological Institute \\ LKPR & Prague International Airport \\ LSTM & Long Short-Term Memory \\ MAE & Mean Absolute Error \\ MLR & Multi-Linear Regression \\ NM & Network Manager \\ rMean & Mean Value of Residuals \\ RMSE & Root-Mean-Square Error \\ rSTD & Residual Standard Deviation \\ RTFM & Regulated Tactical Flight Model \\ \end{tabular}