Predicting Runway Configuration Transition Timings Using Machine Learning Methods

Abstract

Runway configuration change is one of the major factors effecting runway capacity. The transition-time required to change from one runway configuration to another is a key concern in optimising runway configuration. This study formulates prediction of runway transition timings as machine learning regression problem by using an ensemble of regressors which provides continuous estimates using flight trajectories, meteorological data, current and past runway configurations and active STAR routes. The data consolidation and feature engineering convert heterogeneous sources of data and includes a clustering-based prediction of arrival runways on with an 89.9% validity rate. The proposed model is applied on PHL airport with 4 runways and 23 possible configurations. The 6 major runways configuration changes modelled using Random Forest Regressor achieved R2 scores of at least 0.8 and median RMSE of 18.8 minutes, highlighting the predictive power of Machine Learning approach, for informed decision-making in runway configuration change management.