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Framework for determining airport daily departure and arrival delay thresholds: statistical modelling approach
Ronald Wesonga1, Fabian Nabugoomu2
1School of Statistics and Planning, Makerere University, P.O. Box 7062, Kampala, Uganda.
Springerplus
|July 22, 2016
Summary
This study introduces a new framework for airport efficiency by analyzing optimal delay thresholds. A 60% delay threshold was identified as optimal for both arrival and departure operations at Entebbe International Airport.
Area of Science:
- Operations Research
- Transportation Science
- Airport Management
Background:
- Traditional airport efficiency metrics based on minimum turnaround times are insufficient for determining delay proportions.
- Existing theories of delay do not fully capture the complexities of daily airport operational delays.
Purpose of the Study:
- To develop a framework for assessing airport efficiency using optimal arrival and departure delay thresholds.
- To determine the proportion of delay-days at an airport by expanding the theory of delay.
Main Methods:
- A data-driven approach using statistical modeling was applied to identify determinants of daily airport delays.
- Operational data from Entebbe International Airport were used as a case study to test the efficacy of delay threshold levels.
- Multivariate logistic regression was employed to identify optimal delay thresholds.
Main Results:
- Significant differences were observed in the proportions of departure delays (μ = 0.499) and arrival delays (μ = 0.363).
- An optimal daily departure and arrival delay threshold of 60% was confirmed for Entebbe International Airport.
- The optimal threshold was determined based on significant determinants, goodness-of-fit statistics (Wald test), and receiver operating curves.
Conclusions:
- The study proposes a novel modeling framework for measuring airport operational efficiency.
- Findings provide valuable insights for Air Traffic Management in planning and operational efficiency assessments.
- The identified optimal delay thresholds offer a data-driven approach to enhance airport performance evaluation.
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