Zheng, H.; Zhang, J.; Li, H.; Wang, G.; Guo, J.; Wang, J. Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network. Preprints2024, 2024051112. https://doi.org/10.20944/preprints202405.1112.v1
APA Style
Zheng, H., Zhang, J., Li, H., Wang, G., Guo, J., & Wang, J. (2024). Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network. Preprints. https://doi.org/10.20944/preprints202405.1112.v1
Chicago/Turabian Style
Zheng, H., Jianzhong Guo and Jiayao Wang. 2024 "Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network" Preprints. https://doi.org/10.20944/preprints202405.1112.v1
Abstract
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
