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.

21 世纪的现代旅游面临着众多挑战，其中游客数量快速增长、历史城市、博物馆等地区空间有限以及桥梁等瓶颈是最大的问题之一。本文中，我们通过使用深度学习方法（如 RNNs、GNNs 和 Transformers）以及经典的统计 ARIMA 方法在游客流量预测领域进行实证评估，扩展了旅游区域提供的有限数据，并引入了游客的地理位置轨迹、天气和假日等外部数据，以提高预测准确性和处理稀疏数据的能力。