  Modern tourism in the 21st century is facing numerous challenges. One of
these challenges is the rapidly growing number of tourists in space limited
regions such as historical city centers, museums or geographical bottlenecks
like narrow valleys. 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 visitor flow control and prevention of
overcrowding. Static flow control methods like limiting access to hotspots or
using conventional low level controllers could not solve the problem yet. In
this paper, we empirically evaluate the performance of several state-of-the-art
deep-learning methods in the field of visitor flow prediction with limited data
by using available granular data supplied by a tourism region and comparing the
results to ARIMA, a classical statistical method. Our results show that
deep-learning models yield better predictions compared to the ARIMA method,
while both featuring faster inference times and being able to incorporate
additional input features.
