期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3347146.3359094
关键词
Road Network; Machine Learning; Graph Representation Learning; Graph Convolutional Networks
资金
- DiCyPS project
- Obel Family Foundation
- Villum Fonden
The application of machine learning techniques in the selling of road networks holds the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.
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