Air Passenger Demand Forecast in Kerman International Airport

This article analyzes the demand for passenger air transportation in Iran. In analyzing passenger demand, the effect of geographic, socio-economic, and competitive factors has been explored. A data set on landings and takeoffs from the Kerman Airport has been collected for this purpose. An econometric model is first used to predict demand. The significance of all variables is investigated in this study. Once the variables that are of little significance are deleted, a new set of information is created. This information is then processed by the K-Means clustering algorithm and then used as training data for neural network learning. The neural network used for this analysis is an LSTM Deep Learning Network, which has been used to predict passenger demand for years. Finally, with economic and social variables including GDP, income, population, inflation, exchange rate, gasoline prices and oil prices for the coming years, the percentage change in the number of passengers for each year compared to the previous year has been predicted. Based on the inputs of the neural network, changes in air travel demand are determined based on the variables of gross national product, average Kerman income, gasoline price inflation, and oil price inflation. The most important variable is gross national product, which has a profound influence on air travel demand. The accuracy obtained in this method is 83%, which is a very good accuracy for air travel demand.