  We present a novel learning-based approach to graph representations of road
networks employing state-of-the-art graph convolutional neural networks. Our
approach is applied to realistic road networks of 17 cities from Open Street
Map. While edge features are crucial to generate descriptive graph
representations of road networks, graph convolutional networks usually rely on
node features only. We show that the highly representative edge features can
still be integrated into such networks by applying a line graph transformation.
We also propose a method for neighborhood sampling based on a topological
neighborhood composed of both local and global neighbors. We compare the
performance of learning representations using different types of neighborhood
aggregation functions in transductive and inductive tasks and in supervised and
unsupervised learning. Furthermore, we propose a novel aggregation approach,
Graph Attention Isomorphism Network, GAIN. Our results show that GAIN
outperforms state-of-the-art methods on the road type classification problem.
