An artificial neural network for predicting air traffic demand based on socio-economic parameters

Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress. Accurately forecasting the future demand for air travel is crucial for making long-term commitments to these economies. The findings of this study provide valuable insights into the provision of low-cost passenger carrier demand. They can assist low-cost carriers, airports, airport consultants, and government agencies in their strategic planning and development processes. The primary objective of this study is to identify and evaluate artificial intelligence-based solutions, specifically, Artificial Neural Networks (ANN), to determine the most effective model for projecting domestic demand for low-cost carriers. The study analyzes nine variables that impact the accuracy of predicting domestic air passenger demand in emerging countries using a Multilayer Feed-Forward Backpropagation Neural (MFFBPN) network. The performance of the developed ANN model for predicting air passenger demand is assessed through training, validation, and testing using 240 data sets. The evaluation metrics employed, including mean square error (MSE), root mean square error (RMSE), accuracy, precision, sensitivity, and specificity, indicate the effectiveness of the developed ANN model, with results of 0.0019 for MSE, 0.0440 for RMSE, 0.92 for accuracy, 0.92 for precision, 0.93 for sensitivity, and 0.95 for specificity. We show that modeling based on artificial intelligence approaches and ANN is more effective than conventional linear regression models.