A concept of forecasting origin-destination air passenger demand between global city pairs using future socio-economic scenarios

Abstract

This study introduces a concept of a new method of forecasting air passenger flows on a global level using socio-economic scenarios. The method has two steps: forecasting the topology of origin-destination demand network and predicting the number of passengers on existing and new connections. Network theory is applied to simulate demand connections between cities utilizing weighted similarity based algorithms. The number of passengers on a connection is defined using quantitative analogies. Preliminary calculations show promising results. This concept of the global passenger demand prediction will be applied in a modular environment modeling the future air transport system.