Browsing by Subject "transportation economics"

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    Airport selection criteria of low-cost carriers: A fuzzy analytical hierarchy process
    (Elsevier Ltd, 2020) Loh H.S.; Yuen K.F.; Wang X.; Surucu-Balci E.; Balci G.; Zhou Q.
    The selection of airport is an important consideration for low-cost carriers (LCCs) to remain cost competitive. The objective of this study is to identify and rank the airport selection criteria of LCCs. Based on reviewing the existing literature, five main factors comprising 16 sub-factors were developed. The factors were first validated by three industry experts from the aviation industry. Thereafter, a survey questionnaire requiring a comparison of the factors was administered on 28 executives who were involved in the strategy planning and formulation of LCCs based in China or Korea. The collected data were analysed using fuzzy analytical hierarchy process (FAHP). In descending order of their importance, the main factors influencing LCCs' selection of airport are (1) airport charges, (2) airport performance, (3) airport growth opportunities, (4) catchment area and (5) airport infrastructure. The top three sub-factors are airport costs, demand for LCC services and passenger throughput. The research contributes to academic research by providing a holistic assessment of the key considerations influencing LCCs' selection of airport. In addition, it implicates policy formulation of LCCs by providing a framework for assessment of airports that are suitable for LCCs’ operations. © 2019
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    Comparison of artificial neural networks and regression analysis for airway passenger estimation
    (Elsevier Ltd, 2024) Ari D.; Mizrak Ozfirat P.
    With the increasing demand in operations, time is getting more important. In order to use time and energy more effectively, it is becoming more important for airline companies and airport managements to make strategic plans for the future. To make beneficial and correct strategic plans for airways, one of the factors that is needed to be considered is future passenger numbers. With more accurate passenger number forecasts, airport managements can act more efficiently and reduce time, energy consumption and hence would be able to reduce costs. In this study, airway passenger number estimation is handled. Three metropolitan cities’ airport passenger numbers are considered. Artificial neural networks and regression analysis are carried out to estimate passenger number. In addition, data are handled in two different ways. Firstly, ANN and regression analysis are applied using original data series. In the second step, seasonal decomposition is applied on the data series and both approaches are repeated for deseasonal series. In Artificial Neural Networks approach, an experimental design is developed considering training algorithms, number of input nodes and number of nodes in the hidden layer which make up 960 design points. In the results of these experiments, performance of ANN approach is tested for three input factors and high-performance design points are identified. Furthermore, for benchmarking purposes, regression analysis is carried out. Linear, logarithmic, power, exponential, and polynomial models are developed. Finally, results of ANN and regression approaches are compared in terms of mean absolute percent error, and it is found that ANN overperformed compared to regression analysis. © 2024 Elsevier Ltd