Graph Representation Learning for Road Type Classification
This work addresses road type classification for urban planning and navigation systems, representing an incremental improvement with a novel aggregation method.
The paper tackled road type classification by proposing a novel graph representation learning approach that integrates edge features via line graph transformation and introduces a topological neighborhood sampling method, resulting in GAIN outperforming state-of-the-art methods.
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.