Understanding the Capacity of Airport Runways

Abstract

Understanding the capacity of runway system under different operational conditions is of critical importance to airport operators and planners. The availability of granular data on day-to-day runway operations facilitates the development of models that allow a precise comprehension of runway capacity. However, the exercise is empirically challenging due to statistical biases that emerge via the complex interactions between air traffic control and runway capacity. This paper develops a novel causal statistical framework based on a confounding-adjusted Stochastic Frontier Analysis (SFA) to deliver estimates of runway capacity and its parameters that are robust to such biases. The model captures the key factors and interactions affecting runway capacity in a computationally intensive manner. The performance of the model is demonstrated via benchmarking of the estimated capacities of three major airports around the world.