  Machine learning techniques for road networks hold the potential to
facilitate many important transportation applications. Graph Convolutional
Networks (GCNs) are neural networks that are capable of leveraging the
structure of a road network by utilizing information of, e.g., adjacent road
segments. While state-of-the-art GCNs target node classification tasks in
social, citation, and biological networks, machine learning tasks in road
networks differ substantially from such tasks. In road networks, prediction
tasks concern edges representing road segments, and many tasks involve
regression. In addition, road networks differ substantially from the networks
assumed in the GCN literature in terms of the attribute information available
and the network characteristics. Many implicit assumptions of GCNs do therefore
not apply. We introduce the notion of Relational Fusion Network (RFN), a novel
type of GCN designed specifically for machine learning on road networks. In
particular, we propose methods that outperform state-of-the-art GCNs on both a
road segment regression task and a road segment classification task by 32-40%
and 21-24%, respectively. In addition, we provide experimental evidence of the
short-comings of state-of-the-art GCNs in the context of road networks: unlike
our method, they cannot effectively leverage the road network structure for
road segment classification and fail to outperform a regular multi-layer
perceptron.
