A Multi-graph Convolutional Network Framework for Tourist Flow Prediction

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.