Predictability of In-block Time Deviations: An Analysis of Operational Data, Tactical Flight Models and Meteorological Information

Efficient airport operations are vital for ensuring the seamless flow of aircraft and passengers. However, real-world airport operations often deviate from meticulously crafted plans, necessitating dynamic resource allocation. These challenges become particularly pronounced during peak operational periods when butter capacity is limited. To address these issues, we conducted an analysis utilizing operational data from the airport, along with Filed Tactical Flight Model and Current Tactical Flight Model data, as well as meteorological information. Our study primarily focuses on incoming flights at Prague Airport, where we employed regression analysis, forward parameter selection, and a brute-force approach to identify key parameters. These parameters significantly enhance the predictability of in-block time deviations and delay caused in individual phases of the flight. Our model successfully explains up to 96 % of the variability in the data. In summary, this research aims to optimize airport operations by bridging the gap between planned and actual in-block times, ultimately enhancing airport efficiency. The comprehensive analysis of various data elements offers valuable insights for airport management and decision-makers.