  In this work we compare the performance of several machine learning
algorithms applied to the problem of modelling air transport demand.
Forecasting in the air transport industry is an essential part of planning and
managing because of the economic and financial aspects of the industry. The
traditional approach used in airline operations as specified by the
International Civil Aviation Organization is the use of a multiple linear
regression (MLR) model, utilizing cost variables and economic factors. Here,
the performance of models utilizing an artificial neural network (ANN), an
adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support
vector machine, and a regression tree are compared to MLR. The ANN and ANFIS
had the best performance in terms of the lowest mean squared error.
