Urban Overtourism Detection Based on Graph Temporal Convolutional Networks

Urban overtourism results in heavy traffic, degraded tourist experiences, and overloaded infrastructure. Detecting urban overtourism at the early stage is important to minimize the adverse effects. However, urban overtourism detection (UOD) is a challenging task due to ambiguity, sparsity, and complex spatiotemporal relations of overtourism. In this article, we propose a novel UOD framework based on graph temporal convolutional networks (TCNs) to tackle the challenges mentioned above. More specifically, we propose the grid overtourism mode (GOM) to detect urban overtourism on a grid level and propose the overtourism detection mechanism, which gives a quantitative definition of overtourism and screens out the regions where overtourism may occur as candidate regions. Then, we construct the GOM graphs of the candidate regions. Next, we employ the graph TCNs to model the complex spatiotemporal relations of urban overtourism and predict the future GOM graph at the next time interval. Finally, we calculate the urban overtourism scores based on the prediction results. The experiments are conducted based on a real-world dataset. The evaluation results demonstrate the effectiveness of our methods.