Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Bibliographic Data
| ID | 22032139 |
|---|---|
| Authors | Haohua Zheng (Henan University), Jianchen Zhang (0000-0002-0093-0940, Henan University, corresponding author), Heying Li (0009-0006-5025-8941, Henan University), Guangxia Wang (0000-0002-5638-4838, Henan University), Jianzhong Guo (0000-0003-1909-3510, Henan University), Guo Jianzhong (Henan University), Jiayao Wang (0000-0003-2753-6000, Henan University) |
| Year | 2024 |
| Volume | 13 |
| Issue | 9 |
| Pages | 300 |
| Publication date | 2024-08-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13090300 |
| OpenAlex | W4401897375 |
| Language | EN |
| References cited | 27 |
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward 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 the 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 for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. 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
Artificial neural network · Graph · Heterogeneous network · Machine learning · Telecommunications · Wireless network · Advanced Graph Neural Networks · Computer Science · Rough Sets and Fuzzy Logic · Text and Document Classification Technologies · Artificial Intelligence · Theoretical Computer Science
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| Citation velocity | historical |
|---|---|
| Highly cited | No |