  Modern tourism in the 21st century is facing numerous challenges. Among these
the rapidly growing number of tourists visiting space-limited regions like
historical cities, museums and bottlenecks such as bridges is one of the
biggest. In this context, a proper and accurate prediction of tourism volume
and tourism flow within a certain area is important and critical for visitor
management tasks such as sustainable treatment of the environment and
prevention of overcrowding. Static flow control methods like conventional
low-level controllers or limiting access to overcrowded venues could not solve
the problem yet. In this paper, we empirically evaluate the performance of
state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as
well as the classic statistical ARIMA method. Granular limited data supplied by
a tourism region is extended by exogenous data such as geolocation trajectories
of individual tourists, weather and holidays. In the field of visitor flow
prediction with sparse data, we are thereby capable of increasing the accuracy
of our predictions, incorporating modern input feature handling as well as
mapping geolocation data on top of discrete POI data.
