Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation

The Air Traffic Control (ATC) system suffers from an ever-increasing demand for aircraft, leading to capacity issues. For this reason, airspace is regulated by limiting the entry of aircraft into airspace. Knowledge these regulations before they occur would allow ATC be aware conflicting areas airspace, and manage both its human technological resources lessen effect expected regulations. Therefore, paper develops a methodology in which final result machine learning model that allows predicting Predictions shall based mainly on historical data, but also traffic situation at time prediction. results tests sector Spanish are satisfactory. In addition testing results, special emphasis placed explainability model. This will help understand basis predictions validate them operational point view. main conclusion after works well. it possible predict when or not data.