@article{Chen:2025:10.1016/j.trc.2025.104998,
author = {Chen, K and Anupriya and Bansal, P and Anderson, RJ and Findlay, NS and Graham, DJ},
doi = {10.1016/j.trc.2025.104998},
journal = {Transportation Research Part C: Emerging Technologies},
title = {Understanding the capacity of airport runway systems},
url = {http://dx.doi.org/10.1016/j.trc.2025.104998},
volume = {173},
year = {2025}
}

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TY  - JOUR
AB  - Runway systems are often the primary bottlenecks in airport operations. Thus, understanding their capacity is of critical importance to airport operators. However, developing this understanding is not straightforward because, unlike demand or throughput, runway system capacity (RSC) remains unobserved. Moreover, the complex interactions of the physical runway system infrastructure with underlying operating conditions (such as weather) and the airspace result in different capacities under different airport operational scenarios, thereby making the measurement of RSC more complicated. Both analytical and simulation-based approaches need extensive efforts for customization according to specific runway configurations. Analytical models with a moderate level of fidelity are often used to support strategic capacity decisions. In contrast, high-fidelity simulation-based approaches are more appropriate for accommodating wide-ranging operational scenarios and providing accurate RSC estimates to support short-term capacity decisions, though they tend to be resource-intensive. To that end, the availability of granular data on day-to-day runway operations facilitates the development of statistical model that can offer a standardized model specification with minimal customization and provide a precise estimation of RSC for short-term capacity decisions. However, the exercise is empirically challenging due to statistical biases that emerge via the above-mentioned interactions between air traffic flow and control at airports and in the airspace and RSC. This paper develops a novel causal statistical framework based on a confounding-adjusted Stochastic Frontier Analysis (SFA) to deliver estimates of RSC and its parameters that are robust to such biases and are therefore suitable to inform airport operations and planning. The model captures the key factors and interactions affecting RSC in a computationally efficient manner. The performance of the model is benchmarked via a Monte Ca
AU  - Chen,K
AU  - Anupriya
AU  - Bansal,P
AU  - Anderson,RJ
AU  - Findlay,NS
AU  - Graham,DJ
DO  - 10.1016/j.trc.2025.104998
PY  - 2025///
SN  - 0968-090X
TI  - Understanding the capacity of airport runway systems
T2  - Transportation Research Part C: Emerging Technologies
UR  - http://dx.doi.org/10.1016/j.trc.2025.104998
UR  - https://doi.org/10.1016/j.trc.2025.104998
VL  - 173
ER  - 

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