Do aging aircraft run late? Evidence from the united states domestic flights

While aircraft age has long been recognized as a critical factor influencing maintenance costs and safety outcomes, its operational consequences for on-time performance remain comparatively underexplored in empirical research. This study addresses that gap by examining the relationship between aircraft age and departure delays in U.S. domestic aviation. Using the aircraft registry data from Federal Aviation Agency (FAA) and on-time performance data in 2023 from Bureau of Transportation Statistics (BTS). To estimate the impact of aircraft age on departure delays, the study employs both traditional econometric techniques, Ordinary Least Squares (OLS) regression, and advanced ensemble machine learning methods. In addition, SHapley Additive exPlanations (SHAP) analysis is applied to interpret model predictions and quantify the relative contribution of aircraft age in comparison with other explanatory factors at both carrier and aircraft series levels. The results indicate that aircraft age is a statistically significant predictor of departure delay. At the carrier level, most major carriers exhibit a consistent age-delay relationship. At the aircraft series level, Boeing aircraft display more uniform age-related delay patterns, whereas some Airbus narrow-body models show mixed or less consistent results, potentially reflecting differences in design, maintenance practices, or fleet management strategies. These findings extend the literature on fleet aging by moving beyond safety and cost considerations to highlight its role in airline operation, as well as underscore the value of incorporating fleet age into aircraft assignment, scheduling decisions, and performance models.

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Publication Details

Journal
Journal of the Air Transport Research Society
Published
2025-10-28
DOI
https://doi.org/10.1016/j.jatrs.2025.100090
Primary Topic
Reliability and Maintenance Optimization
Type
article
Field-Weighted Citation Impact
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article

Do aging aircraft run late? Evidence from the united states domestic flights

Yi Gao, Jiayu Liu, 智也 喜熨斗, Sen Wang
Journal of the Air Transport Research Society
Reliability and Maintenance Optimization
article

Do aging aircraft run late? Evidence from the united states domestic flights

Yi Gao, Jiayu Liu, 智也 喜熨斗, Sen Wang
article en

Abstract

While aircraft age has long been recognized as a critical factor influencing maintenance costs and safety outcomes, its operational consequences for on-time performance remain comparatively underexplored in empirical research. This study addresses that gap by examining the relationship between aircraft age and departure delays in U.S. domestic aviation. Using the aircraft registry data from Federal Aviation Agency (FAA) and on-time performance data in 2023 from Bureau of Transportation Statistics (BTS). To estimate the impact of aircraft age on departure delays, the study employs both traditional econometric techniques, Ordinary Least Squares (OLS) regression, and advanced ensemble machine learning methods. In addition, SHapley Additive exPlanations (SHAP) analysis is applied to interpret model predictions and quantify the relative contribution of aircraft age in comparison with other explanatory factors at both carrier and aircraft series levels. The results indicate that aircraft age is a statistically significant predictor of departure delay. At the carrier level, most major carriers exhibit a consistent age-delay relationship. At the aircraft series level, Boeing aircraft display more uniform age-related delay patterns, whereas some Airbus narrow-body models show mixed or less consistent results, potentially reflecting differences in design, maintenance practices, or fleet management strategies. These findings extend the literature on fleet aging by moving beyond safety and cost considerations to highlight its role in airline operation, as well as underscore the value of incorporating fleet age into aircraft assignment, scheduling decisions, and performance models.

Journal of the Air Transport Research SocietyVol. 5
Purdue University West Lafayette (US), The Ohio State University (US)
Openalex Percentile: Top 61%
Reliability and Maintenance Optimization
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