Nation-wide touristic flow prediction with Graph Neural Networks and heterogeneous open data.

Tourism has become a very active ecosystem to deploy solutions based on Information and Communication Technologies. Indeed, it is now possible to analyse the mobility behaviour of tourists in great detail. However, current solutions aimed at anticipating tourist flows usually follow a limited approach based on the local (e.g., to predict the next landmark to visit) or regional (e.g., to predict the incoming number of tourists in a city) level. This paper states a novel approach to solve the problem of tourist inflow forecasting on a broader nationwide scale by defining it as an edge prediction task. To do so, we model the tourist mobility of a country as a graph which fuses heterogeneous tourism data obtained from multiple sources related to the country's mobility and infrastructure features. Then, as a major contribution, an ensemble of Graph Neural Networks are fed with the graph models to provide the final prediction. The proposed solution has been tested in Spain showing a F1 score higher than 0.7. • A mechanism to anticipate the flows of tourists in Spain is proposed. • The prediction is solved as an edge prediction between Spanish regions. • A Graph Neural Network has been used as underlying model. • The model is fed with touristic infrastructure data of Spain. • The human mobility is extracted from Call Detail Records and geo-tagged Twitter posts. [ABSTRACT FROM AUTHOR]