## 5 Conclusions  
This article presents an analysis of the current technical and technological approaches for anticipating flight progress, which is increasingly critical for airport stakeholders involved in ground operations. Effective airport resource allocation is essential for timely aircraft accommodation at the stand, with appropriate turnaround services being a prerequisite for maintaining on-time performance and minimizing reactionary delays. However, for efficient resource allocation, it is necessary to have clear visibility of when an aircraft is expected to arrive on the blocks. Typically, when operations are nominal and no extraordinary events occur, flights tend to operate on schedule. Early or delayed arrivals are frequently encountered, particularly in busy airports and airspace settings. Various factors, such as technical malfunctions, adverse weather, industrial actions, or other unforeseen events, can disrupt both en-route and ground operations. While some of these occurrences are unpredictable and spontaneous, others--such as recurring weather patterns or operational delays--can be somewhat anticipated based on available data. Despite the availability of data linked to meteorological, flight, or airport operations that can help anticipate delays, many European airports, with the exception of a few major hubs, face challenges in developing capabilities to predict deviations between actual and scheduled flight performance. These airports often rely on local human-machine interfaces, fed by manual or automated updates triggered by aircraft or radar reports. The challenge becomes even greater with intercontinental traffic, as a significant portion of the flight trajectory, occurs outside the ECAC region, limiting the ability of airports to monitor progress and detect delays early enough to make timely adjustments to resource allocation. The goal of this study was to enhance airport resilience by developing predictive capabilities to anticipate early and late arrivals. Specifically, this study aimed to predict in-block deviations (\(\delta_{IBT}\)). However, the results revealed that the model's performance in predicting in-block time deviations was not as reliable as expected. Despite employing a suitable machine learning technique for the nature of the data, the model struggled to capture the complex interdependencies affecting \(\delta_{IBT}\). It appears that the volume and quality of the input data were insufficient, leading to suboptimal model performance. Although the objective of predicting in-block deviations was technically achieved, the accuracy of the results limits their practical application. The limitations encountered in predicting \(\delta_{IBT}\) led to a shift in focus toward predicting landing times (\(\delta_{LT}\)), which emerged as a more successful aspect of this study. The ability to predict landing times not only addressed the challenges faced with in-block time predictions but also proved to be a valuable outcome. The model exhibited a low prediction error for \(\delta_{LT}\), with an RMSE of 8.71 min, positioning it as a potentially valuable tool for airport stakeholders. This degree of accuracy enables airports to forecast flight arrivals with enough precision to align with the standard on-time performance window of \(-15\) to +15 min. Such predictions could greatly improve operational planning and responsiveness, particularly in real-time resource allocation and runway usage. The success of the landing time predictions raises important questions about how to further enhance the accuracy of in-block time predictions. Future research could focus on developing models specifically for predicting taxi-in time and parking position availability, which could complement the existing model's capabilities. These additional models would allow for the better anticipation of \(\delta_{IBT}\) by considering factors such as taxiway layout, preceding traffic, runway in use, and real-time stand availability.