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${}^1$. Our results show that GAIN outperforms state-of-the-art methods on the road type classification problem.

我们提出了一种新的基于学习的方法来使用最先进的图形卷积神经网络来表示道路网络的图形表示。我们的方法应用于来自 Open Street Map 的 17 个城市的现实道路网络。虽然边缘特征对于生成道路网络的描述性图表示至关重要，但图卷积网络通常仅依赖于节点特征。我们表明，通过应用线图变换，仍然可以将具有高度代表性的边缘特征集成到此类网络中。我们还提出了一种基于由局部和全局邻居组成的拓扑邻域的邻域采样方法。我们比较了在转导和归纳任务以及有监督和无监督学习中使用不同类型的邻域聚合函数的学习表示的性能。${}^1$. 我们的结果表明，GAIN 在道路类型分类问题上优于最先进的方法。