Flight delay has long been a critical issue affecting the operational efficiency and economic performance of airlines.While there are various methods for predicting flight delay times,they often suf-fer from challenges such as low accuracy and incomplete consideration of influencing factors.To address these issues,a data-driven indirect prediction model for flight delay time is proposed.This model,based on data from the Airport Collaborative Decision Making(ACDM)system,employs the random forest al-gorithm to predict directly the aircraft's dwell time on the apron and the final departure time,from which the flight delay time is calculated.Validation using experimental data demonstrates a 100％ accu-racy rate when evaluated against the 15-minute flight delay standard.This model can support airlines in predicting flight delays,enabling targeted optimization of fleet operation processes to enhance operational efficiency.
维普
万方数据
