Machine Learning for Air Transport Planning and Management

TL;DR

This paper compares various machine learning algorithms, including ANN and ANFIS, for modeling air transport demand, highlighting their superior performance over traditional linear regression methods.

Contribution

It provides a comparative analysis of multiple machine learning models for air transport demand forecasting, demonstrating the effectiveness of neural network-based approaches.

Findings

ANN and ANFIS outperform other models in accuracy

Machine learning models improve demand forecasting

Traditional MLR is less effective

Abstract

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.