Forecasting air traveling demand for Saudi Arabia’s low cost carriers

Abstract

The recent revolution of Airline market sector in Saudi Arabia has brought more attentions to air travel demand forecasting. New low cost carriers (LCCs) with potential of delivering an affordable services to customers have been established. While there are many academic literature on passenger demand forecasting, there has not been any reported study that capture the effect and impacts of Islamic holidays in forecasting Saudi Arabia’s LCCs passenger demand. We approach this issue by investigating the improvement of forecasting Saudi Arabia’s low cost carriers (LCCs) passenger demand using machine learning techniques by accounting the Islamic Holidays. For this research, King Khalid International Airport air passenger demand will be analyzed. Our aim is to apply different forecasting models: Genetic Algorithm, Artificial Neural Network and Classical linear regression to forecast Saudi Arabia’s domestic LCC passenger demand. The model’s performance will be evaluated using mean absolute percentage error (MAPE).