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The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms by Ersin Korkmaz; Ali Payıdar Akgüngör is a Computer Science article available to read on EtoBox.
What is The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms about?
## Abstract The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta‐heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport pa
Who reads The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms?
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- Author
- Ersin Korkmaz; Ali Payıdar Akgüngör
- Publisher
- Wiley
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)