Abstract
The aim of this paper is to design an approach to evaluate the expected efficiency and performance of future airport infrastructure. First, an airport sampling method to select similar airports is developed based on socioeconomic and operational airport variables that are summarized in a proxy variable; second, the ARIMA-GARCH-Bootstrap method is applied to forecast the selected outputs (PAX and ATMS) whilst the selected inputs (Cities, Gates, Runaways, Airport Size, Pax carriers, and Num. of employees) remain constant; and third, the VRS-OO and the CRS-OO DEA models are implemented to evaluate the efficiency and performance of the airports in the current and future years. The proposed approach is used to evaluate the future airport infrastructure of the new Mexico City Airport against 19 representative worldwide airport hubs. The proposed approach is applied to analyze the Mexico City Airport multi-airport system infrastructure as a case study. The results show that this multi-airport system requires more airside infrastructure that must be added by the new Mexico City Airport, airlines should operate aircrafts with more capacity to serve more PAX per ATM, and airlines must open new connections at the new Mexico City Airport to increase the expected efficiency and performance of this multi-airport system.
Highlights
The air transport industry highly contributes to the development of the economy and society of any country in the world, mainly because it generates jobs and stimulates social and economic activities [1]
Data Envelopment Analysis (DEA) models can measure efficiency in two different orientations: input-oriented DEA models determine the minimum input needed, if used efficiently, to achieve the same output and output-oriented DEA models determine the maximum potential output that can be reached if the given inputs are used efficiently [41]. With these arguments in mind, in this paper, we use the constant returns-to-scale DEA model (CRS) model proposed by [38] from an output orientation (CRS-OO) to plan future airport infrastructure performance. We choose this DEA model because [3,42] found that airports operate under a constant return on scale and because it is possible to assume that, in this study, the selected inputs (Cities, Gates, Runaways, Airport Size, Pax carriers, and Num of employees) and the selected outputs (PAX and ATMS) can be classified as physical levels because these measures are actual amounts of products that are often assumed to be proportional to resources, and they satisfy the assumption of proportionality [40]
In the third step, the variable return-to-scale DEA model (VRS)-OO and the CRS-OO DEA models are used to evaluate the performance of the airports in the sample in current and future years
Summary
The air transport industry highly contributes to the development of the economy and society of any country in the world, mainly because it generates jobs and stimulates social and economic activities [1]. It is important to plan new airport infrastructure based on future performance, analyzing actual and forecasting data and proposing management strategies to assure that future airport infrastructure will be as efficient and sustainable as possible [4,5,6]. The precision of a cost-benefit analysis depends mainly on the accuracy of traffic forecasts (enplaned passenger demand (PAX) and air transport movements (ATMS)) and construction costs forecasts [8]. New airports might be inefficient, and airport managers must design methodologies to develop and implement airport management strategies in advance to assure efficiency of future airport infrastructure
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