Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks

Abstract

This study employs back-propagation neural networks (BPN) to improve the forecasting accuracy of air passenger and air cargo demand from Japan to Taiwan. The factors which influence air passenger and air cargo demand are identified, evaluated and analysed in detail. The results reveal that some factors influence both passenger and cargo demand, and the others only one of them. The forecasting accuracy of air passenger and air cargo demand has been improved efficiently by the proposed procedure to evaluate input variables. The established model improves dramatically the forecasting accuracy of air passenger demand with an extremely low mean absolute percentage error (MAPE) of 0.34% and 7.74% for air cargo demand.

Keywords:

• forecast
• demand
• air passenger
• air cargo
• back-propagation neural networks

Acknowledgements

The authors wish to thank the National Science Council of the ROC (Taiwan) for financially supporting this research under contract no. NSC 98-2221-E-156-006.