RT journal article
T1 Nation-wide touristic flow prediction with Graph Neural Networks and heterogeneous open data
A1 Terroso Sáenz, F.
A1 Arcas-Tunez, F.
A1 Muñoz Ortega, Andrés
A2 Ingeniería Informática
K1 Graph Neural Networks
K1 Online Social Networks
K1 Open data
K1 Touristic mobility
K1 Twitter
AB 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.
PB Elsevier B.V.
SN 1566-2535
YR 2023
FD 2023
LK http://hdl.handle.net/10498/32835
UL http://hdl.handle.net/10498/32835
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026