Article2021

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

14 citations0/0 files storedConcurrency and Computation Practice and Experience

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Aviation Industry Analysis and Trends Metaheuristic Optimization Algorithms Research Forecasting Techniques and Applications

Abstract

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 passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.

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2006Another look at measures of forecast accuracy 2009Air transport demand and economic growth in Brazil: A time series analysis 2013Transit network design by Bee Colony Optimization 2009Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework 2018Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach 2014A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting 2013Demand Forecasting for Domestic Air Transportation in Turkey 2018A new direct demand model of long-term forecasting air passengers and air transport movements at German airports

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