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Enhancing Airport Resource Efficiency Through Statistical Modeling of Heavy-Tailed Service Durations: A Case Study on Potable Water Trucks

Changcheng Li,Minghua Hu,Yuxin Hu,Zheng Zhao,Yanjun Wang

Published 2025 in Aerospace

ABSTRACT

In airport operations management, accurately estimating the service durations of ground support equipment such as Potable Water Trucks (PWTs) is essential for improving resource allocation efficiency and ensuring timely aircraft turnaround. Traditional estimation methods often use fixed averages or assume normal distributions, failing to capture real-world variability and extreme scenarios effectively. To address these limitations, this study performs a comprehensive statistical analysis of PWT service durations using operational data from Beijing Daxing International Airport (ZBAD) and Shanghai Pudong International Airport (ZSPD). Employing chi-square goodness-of-fit tests, twenty probability distributions—including several heavy-tailed candidates—were rigorously evaluated under segmented scenarios, such as peak versus non-peak periods, varying temperature conditions, and different aircraft sizes. Results reveal that heavy-tailed distributions offer context-dependent advantages: the stable distribution exhibits superior modeling performance during peak operational periods, whereas the Burr distribution excels under non-peak conditions. Interestingly, contrary to existing operational assumptions, service durations at extremely high and low temperatures showed no significant statistical differences, prompting a reconsideration of temperature-dependent planning practices. Additionally, analysis by aircraft category showed that the Burr distribution best described service durations for large aircraft, while stable and log-logistic distributions were optimal for medium-sized aircraft. Numerical simulations confirmed these findings, demonstrating that the proposed heavy-tailed probabilistic models significantly improved resource prediction accuracy, reducing estimation errors by 13% to 25% compared to conventional methods. This research uniquely demonstrates the practical effectiveness of employing context-sensitive heavy-tailed distributions, substantially enhancing resource efficiency and operational reliability in airport ground handling management.

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