Link and edge weight prediction in air transport networks — An RNN approach

Abstract

Predicting the future structure of air transport networks is important for several stakeholders in terms of e.g., access to markets, prospects for economic integration and development of regions. Link and edge weight prediction aims to foretell whether two airports will be connected by a direct flight in a future stage of the development of a network and the frequency with which services will be provided. This work assesses the capacity of popular similarity-based algorithms to predict network evolution in air transport. It also proposes a supervised recurrent neural network-based learning framework (RNN) for link prediction. It draws on a set of topological, temporal and content-based features. Experimental results from network data that maps the European Air Transport Network in the period between 2010 and 2019 show that similarity-based algorithms are not able to predict future network stages well. Their performance in predicting newly emerging links remains below expectations formulated in the earlier link prediction literature. The proposed RNN framework outperforms traditional similarity algorithms by a substantial margin. However, the results suggest that link and edge weight prediction remain challenging in sparse air transport networks. Predictive performance must be optimised even further before forecasts can be used to inform concrete policy decisions.