Do aging aircraft run late? Evidence from the united states domestic flights
Year: 2025
Type: article
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.
Authors: Jiayu Liu, Sen Wang, Zhi Dou, Yi Gao
Institutions: Purdue University West Lafayette, The Ohio State University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,600
OpenAlex ID: W4415629350