Airport Time Profile Construction Driven by Flight Delay Prediction

Abstract

The time structure is the equilibrium outcome resulting from the interaction between market demand and the availability of time resources, with time parameters reflecting the operational support capability of the aviation system.The construction of time parameters and time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency.Constructing a reasonable 18–24 hour timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management.This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and Partial Least Squares Regression. Additionally, Ensemble learning is employed to forecast flight delay levels. The findings demonstrate that Random Forest yields favorable results in regression and prediction tasks, allowing for the integration of upper(good weather) and lower(severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 minutes.That is, it meets the expectations of flight normality.