Relational Fusion Networks: Graph Convolutional Networks for Road Networks

Abstract

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.

arXiv commentIEEE Transactions on Intelligent Transportation Systems (2020). arXiv admin note: substantial text overlap with arXiv:1908.11567

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• Adam: A Method for Stochastic Optimization
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• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
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Cited by in corpus (3)

• Graph Neural Network for Traffic Forecasting: A Survey
• Graph Representation Learning for Road Type Classification
• Spatial Heterophily Aware Graph Neural Networks