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.

中文翻译：

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一种用于旅游流量预测的多图卷积网络框架

随着网络物理系统和社会物联网的进步，旅游业面临着挑战和机遇。我们现在可以收集、存储和分析大量的旅行数据。借助数据科学和人工智能，智慧旅游为游客提供了极大的自主性和便利性，实现了智能出行。充分利用这些海量数据，为智慧旅游提供更好的服务具有重要意义。然而，由于不同地点的兴趣点的访问存在偏差和不平衡，因此预测每个地点的游客流量具有重要意义，可以帮助服务提供者提前设计更好的时间表访问策略。在此背景下，本文提出了一个多图卷积网络框架，命名为 AMOUNT，用于旅游流量预测。为了捕捉 POI 之间的多样化关系，AMOUNT 首先构建了三个子图，包括地理图、交互图和关联图。然后，利用多图卷积网络来预测未来的旅游流量。在两个真实世界数据集上的实验结果表明，所提出的 AMOUNT 模型优于所有其他基线旅游流量预测方法。