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.

翻译：21世纪的现代旅游业正面临诸多挑战，其中，游客数量快速增长并涌入历史名城、博物馆及桥梁等空间有限区域的问题尤为突出。在此背景下，对特定区域的旅游规模和旅游流量进行准确预测，对于可持续环境保护、防止过度拥挤等游客管理任务至关重要。传统的低层级控制器或限制进入拥挤场所等静态流量控制方法至今未能解决这一问题。本文通过实证评估了循环神经网络（RNN）、图神经网络（GNN）和Transformer等先进深度学习方法以及经典统计ARIMA方法的性能。我们以某旅游区域提供的有限粒度数据为基础，引入游客个体地理定位轨迹、天气和节假日等外部数据加以扩展。在稀疏数据的游客流量预测领域，我们通过引入现代输入特征处理方法以及将地理定位数据映射到离散兴趣点（POI）数据之上，从而能够提高预测精度。