Using an artificial neural network approach to forecast Australia's domestic passenger air travel demand

Panarat Srisaeng · Glenn Baxter · Graham Wild

24 citations0/0 files storedWorld Review of Intermodal Transportation Research

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Aviation Industry Analysis and Trends Forecasting Techniques and Applications Air Traffic Management and Optimization

Abstract

The aim of this work is to utilise an artificial neural network (ANN) to model Australia's domestic air travel demand. This modelling will then facilitate forecasting future passenger demand. Forecasting passenger demand is a critical issue in the air transport industry and is generally viewed as the most crucial function of airline management. This is the first time an ANN has been applied to domestic air travel in Australia, with ANN approaches having limited use in the industry. Two ANN models to forecast Australia's domestic airline passenger demand (PAX model) and revenue passenger kilometres performed (RPKs model) were constructed. Quarterly data from 1992 to 2014 was used. Australia's real interest rates and tourism attractiveness were included as candidate variables for the first time in the models. As with the conventional ICAO approach to forecasting, GDP and airfare were significant factors, along with unemployment, jet fuel, and accommodation beds due to the large portion of the market to tourism.

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2000The M3-Competition: results, conclusions and implications 2005Air transport and tourism—Perspectives and challenges for destinations, airlines and governments 2005Designing an artificial neural network for forecasting tourism time series 2011Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction 2013Forecasting tourism demand to Catalonia: Neural networks vs. time series models 2008A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis 1995Neural networks for financial forecasting 1985Flying Off Course: Airline Economics and Marketing

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