Quantifying the relationship between airport capacity and delay using causal inference methods
File(s)
Author(s)
Chen, Kailin
Type
Thesis
Abstract
Global air traffic is growing due to economic expansion, increased connectivity, rising middle-class incomes, and post-pandemic recovery. Consequently, most airports are operating at or near capacity, causing congestion-related delays. Addressing these challenges requires effective operational policies and strategic planning for airport capacity. To inform such efforts, this thesis aims to analyze the relationship between airport capacity and delay at both operational and strategic levels. Existing literature examines these relationships from an associational perspective, failing to capture true impacts of capacity on delay. This thesis is the first to analyze causal relationships, providing empirical insights reproducible across diverse operational scenarios, leveraging high-granularity operational data and advanced causal inference models. The analysis is structured around three themes: the first two addressing operational aspects and the third on strategic considerations.
First, this thesis proposes an empirical model to estimate runway system capacity across varying operational scenarios. The estimation employs confounding-adjusted Stochastic Frontier Analysis controlling for unobserved operational factors such as air traffic control that could confound the results.
Secondly, the technology driving congestion in airport surface operations is explored by modeling surface delay versus runway and ground capacity utilization, using runway capacity estimates from the first theme. This congestion technology (CT) is estimated using Bayesian non-parametric instrumental variables to address confounding from unobserved operational factors. The CT delivers key insights into surface-use efficiency such as optimum operating capacity utilization.
Finally, this thesis evaluates how strategic capacity interventions alleviate delays. A causal statistical framework built on sharp Regression Discontinuity in Time adjusts for biases from interaction of capacity intervention, demand, and delay. This framework is applied to yield unbiased empirical effects of two infrastructure expansions on delay, passengers, airlines, the environment, and safety.
These methodological contributions and findings carry significant implications for both daily operation management and strategic investment planning.
First, this thesis proposes an empirical model to estimate runway system capacity across varying operational scenarios. The estimation employs confounding-adjusted Stochastic Frontier Analysis controlling for unobserved operational factors such as air traffic control that could confound the results.
Secondly, the technology driving congestion in airport surface operations is explored by modeling surface delay versus runway and ground capacity utilization, using runway capacity estimates from the first theme. This congestion technology (CT) is estimated using Bayesian non-parametric instrumental variables to address confounding from unobserved operational factors. The CT delivers key insights into surface-use efficiency such as optimum operating capacity utilization.
Finally, this thesis evaluates how strategic capacity interventions alleviate delays. A causal statistical framework built on sharp Regression Discontinuity in Time adjusts for biases from interaction of capacity intervention, demand, and delay. This framework is applied to yield unbiased empirical effects of two infrastructure expansions on delay, passengers, airlines, the environment, and safety.
These methodological contributions and findings carry significant implications for both daily operation management and strategic investment planning.
Version
Open Access
Date Issued
2024-07-31
Date Awarded
01/01/2025
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Graham, Daniel J.
Anderson, Richard J.
Publisher Department
Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)