The application of machine learning techniques inthe setting of road networks holds the potential to facilitate many important intelligent 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 outperform state-of-the-art GCN architectures by up to 21–40% on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks
TidsskriftIEEE Transactions on Intelligent Transportation Systems
Sider (fra-til)1-12
Antal sider12
StatusUdgivet - 14 aug. 2020

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