Forecasting global air passenger demand network using weighted similarity-based algorithms

Abstract

The aim of this study is to define an appropriate approach to forecast the appearance of the air passenger demand between cities worldwide. For air passenger demand link forecasting a weighted similarity-based algorithm is used, with an analysis of nine indices. The weighted resource allocation index demonstrates the best metrics. The accuracy of this method has been determined through a comparison of modeled and known data from three separate years. The known data was used to establish boundaries when applying the similarity-based algorithm. As a result, it was found that a weighted resource allocation index, with defined boundaries, should be utilized for link prediction in the air passenger demand network. Furthermore, it is shown that grouping cities within the air passenger demand network, based on socio-economic indicators, increases the accuracy of the forecast.