  Tourists often go to multiple tourism destinations in one trip. The volume of
tourism flow between tourism destinations, also referred to as ITF
(Inter-Destination Tourism Flow) in this paper, is commonly used for tourism
management on tasks like the classification of destinations' roles and
visitation pattern mining. However, the ITF is hard to get due to the
limitation of data collection techniques and privacy issues. It is difficult to
understand how the volume of ITF is influenced by features of the
multi-attraction system. To address these challenges, we utilized multi-source
datasets and proposed a graph-based hybrid deep learning model to predict the
ITF. The model makes use of both the explicit features of individual tourism
attractions and the implicit features of the interactions between multiple
attractions. Experiments on ITF data extracted from crowdsourced tourists'
travel notes about the city of Beijing verified the usefulness of the proposed
model. Besides, we analyze how different features of tourism attractions
influence the volume of ITF with explainable AI techniques. Results show that
popularity, quality and distance are the main three influential factors. Other
features like coordinates will also exert an influence in different ways. The
predicted ITF data can be further used for various downstream tasks in tourism
management. The research also deepens the understanding of tourists' visiting
choice in a tourism system consisting of multiple attractions.
