Research on Evaluation Model of Road Congestion of Tourism Scenic Spots Based on Spatial Syntax and Artificial Neural Network Method—A Case of Kulangsu Island, Xiamen, China

A novel evaluation model combining Spatial Syntax and Artificial Neural Network (ANN) effectively predicts road congestion in tourism scenic spots, addressing the disconnect between land-use and transportation planning. The study reveals that high-attractiveness areas in Kulangsu Island are concentrated along its edges, while the model accurately identifies issues with road distribution. By leveraging ANN's dynamic assignment and Space Syntax's accessibility analysis, the approach provides a robust tool for future tourism space management, offering insights into crowd flow patterns and network structure inconsistencies.

Su, Q., Chen, X., Xiao, J., Yu, X., & Cui, T. (2022). Research on Evaluation Model of Road Congestion of Tourism Scenic Spots Based on Spatial Syntax and Artificial Neural Network Method—A Case of Kulangsu Island, Xiamen, China. 2022 International Conference on Computational Infrastructure and Urban Planning, 69–74. https://doi.org/10.1145/3546632.3546882