Machine Learned Prediction of Runway Configuration Transition Times for Capacity Analysis

Abstract

The runway system of an airport is a bottleneck resource, limiting the amount of traffic an airport can service. The capacity of a runway system is affected by the runway configuration in use and the transition time to change to a new runway configuration. Better prediction of runway configuration transition times can aid air traffic controllers in selecting the runway configuration that minimises delays. This study introduces a novel data-driven approach to model the transition times between directional runway configuration changes, derived by using computed features from the flight positional data. The study also formulates classification models to assign the magnitude of transition times and their impact on runway capacity, utilizing features known in the literature, as well as engineered features including weather coefficients and runway complexity. The transition time model is able to identify the instances where the transition times are ‘High’ approximately 92% of the time. Correctly identifying ‘High’ transition times is important as high transition times lead to greater reduction in runway capacity. This is validated when the predicted transition time is used as a feature input for the capacity impact model, which correctly identifies periods of unfulfilled demand approximately 89% of the time. The predicted transition time is shown to be a more important predictor of capacity impact than weather features, which to date have been considered crucial features in determining capacity.