Relational Fusion Networks: Graph Convolutional Networks for Road Networks

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 im...

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Published in:arXiv.org 2020-09
Main Authors: Jepsen, Tobias Skovgaard, Jensen, Christian S, Nielsen, Thomas Dyhre
Format: Article
Language:English
Subjects:
Artificial neural networks
Cognitive tasks
Foreign operations of US corporations
Machine learning
Roads
Superstores
Transportation applications
Transportation networks
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description 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.
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subjects Artificial neural networks
Cognitive tasks
Foreign operations of US corporations
Machine learning
Roads
Superstores
Transportation applications
Transportation networks
title Relational Fusion Networks: Graph Convolutional Networks for Road Networks
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