Air Transport Demand Forecasting in Routes Network by Artificial Neural Networks

摘要

Aviation industry relies strongly on air transport demand forecasting for developing the operation strategy. Time series analysis, gravity model, grey theory and artificial neural networks are familiar tools for forecasting air traffic. In this article, artificial neural networks were employed to establish mathematical model with multiple inputs and multiple outputs, which is different from time series analysis and grey theory considered only single input and single output. The traditional analyses for air transport demand forecasting by artificial neural networks consider single output, as single airport or one route is considered in general. This research overcomes the shortcomings of time series analysis and grey theory. It also improves the weakness of gravity model that must confirm the explicit equation in advance. The results indicate that the novel model may accurately forecast the air transport demand in routes network.