Interaction strength prediction based on neural networks of graph scrolls considering geographical features

Yi Sun; Lin Liu

doi:10.1117/12.2684217

14 June 2023 Interaction strength prediction based on neural networks of graph scrolls considering geographical features

Yi Sun, Lin Liu

Author Affiliations +

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127081N (2023) https://doi.org/10.1117/12.2684217
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

Accurate interaction strength prediction plays an important role in urban planning and development. Based on the existing national interaction intensity data between cities, the research uses GCN model, ChebNet model and GAT model based on graph convolutional neural network to realize the prediction of urban interaction intensity, and determines the optimal model through accuracy evaluation. Further we introduce geographical features, optimize the optimal GAT model, and finally the graph neural network model considering geographical characteristics is constructed to realize real-time, efficient, and long-term prediction of urban interaction intensity. It is found that there is a strong interaction betweend cities.

Citation Download Citation

Yi Sun and Lin Liu "Interaction strength prediction based on neural networks of graph scrolls considering geographical features", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127081N (14 June 2023); https://doi.org/10.1117/12.2684217

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