# Relational Fusion Networks: Graph Convolutional Networks for Road Networks

arXiv: https://arxiv.org/abs/2006.09030 · HTML: https://mlsanity.com/paper/2006.09030/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

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

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [City Foundation Models for Learning General Purpose Representations from OpenStreetMap](https://mlsanity.com/paper/2310.00583/) (2023)
- [Road Network Representation Learning: A Dual Graph based Approach](https://mlsanity.com/paper/2304.07298/) (2023)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Graph Representation Learning for Road Type Classification](https://mlsanity.com/paper/2107.07791/) (2021)
- [Spatial Heterophily Aware Graph Neural Networks](https://mlsanity.com/paper/2306.12139/) (2023)
- [Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics](https://mlsanity.com/paper/2403.11495/) (2024)
- [Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness](https://mlsanity.com/paper/2112.03404/) (2021)
- [GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation](https://mlsanity.com/paper/2207.00838/) (2022)
- [Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction](https://mlsanity.com/paper/2207.11034/) (2022)
- [Learning to integrate vision data into road network data](https://mlsanity.com/paper/2112.10624/) (2021)
- [Hierarchical Frequency-Decomposition Graph Neural Networks for Road Network Representation Learning](https://mlsanity.com/paper/2511.12507/) (2025)
- [UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation](https://mlsanity.com/paper/2104.13321/) (2021)
- [Visual Feature Encoding for GNNs on Road Networks](https://mlsanity.com/paper/2203.01187/) (2022)
- [Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model](https://mlsanity.com/paper/2209.03629/) (2022)
- [Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning](https://mlsanity.com/paper/2510.14819/) (2025)
- [NARA: Anchor-Conditioned Representation Learning for Heterogeneous Vector Geoentities](https://mlsanity.com/paper/2605.12276/) (2026)

## Abstract (arXiv)

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCNs by 21%-40% on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks.