# Graph Convolutional Networks for Road Networks

arXiv: https://arxiv.org/abs/1908.11567 · HTML: https://mlsanity.com/paper/1908.11567/

## Key facts

- Cited by 17 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 17 of 17)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Jointly Contrastive Representation Learning on Road Network and Trajectory](https://mlsanity.com/paper/2209.06389/) (2022)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Road Network Representation Learning with the Third Law of Geography](https://mlsanity.com/paper/2406.04038/) (2024)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.08415/) (2022)
- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)
- [Holistic Semantic Representation for Navigational Trajectory Generation](https://mlsanity.com/paper/2501.02737/) (2025)
- [Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric](https://mlsanity.com/paper/2302.01259/) (2023)
- [Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics](https://mlsanity.com/paper/2403.11495/) (2024)
- [Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data](https://mlsanity.com/paper/2402.05153/) (2024)
- [TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation](https://mlsanity.com/paper/2605.10020/) (2026)
- [Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2402.06135/) (2024)
- [Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning](https://mlsanity.com/paper/2511.06633/) (2025)
- [Treatment Effect Estimation for Graph-Structured Targets](https://mlsanity.com/paper/2412.20436/) (2024)
- [MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactions](https://mlsanity.com/paper/2509.05685/) (2025)
- [LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction](https://mlsanity.com/paper/2602.23290/) (2026)

## Abstract (arXiv)

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.