Airport delay prediction using machine learning regression models as a tool for decision making process
Air traffic has seen a steady increase in the number of operations in recent years. This increase was not accompanied by an expansion of the system’s capacity to continue operations without significant impacts on the performance and quality of transport services. It is very difficult to expand the airport infrastructure in today’s conditions, so it is necessary to optimize the existing operations to increase the capacity and efficiency of the system. The paper focused on determining the correlation of individual key indicators of airport performance and their impact on delays, which was observed as an output variable. Three machine learning regression methods were applied, Decision Tree, Random Forrest and Support Vector Regression to predict delays with respect to other characteristics of traffic and surrounding airspace. All three methods were able to predict a delay with an error of ±60s in at least 91% of the test set, which shows a clear potential for the use of machine learning in this domain.