Tourism demand forecasting using complex network theory

Tourism demand forecasting is proven essential to optimize management, increase revenues, and control risks. Spatial effects have been widely considered in forecasting models and achieved good results. However, existing studies paid little attention to the dynamicity and hierarchy of spatial correlations among tourist attractions within tourism destination complex networks. This study addresses this paucity by proposing a novel deep learning model, namely, multi-hierarchy dynamic spatiotemporal network (MHDSTN). Innovatively, dynamic and hierarchical spatiotemporal features are extracted for forecasting improvement by integrating transformer, gated graph convolution, and long short-term memory. The effectiveness, robustness, and interpretability of the model are demonstrated through an empirical case in Beijing, China. Results indicate that incorporating dynamic and hierarchical spatial effects remarkably improves forecasting accuracy.