Relational Fusion Networks: Graph Convolutional Networks for Road Networks

Tobias Skovgaard Jepsen*, Christian S. Jensen, Thomas Dyhre Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)
33 Downloads (Pure)

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 Graph Convolutional Network (GCN) designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCN architectures by up to 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.

Original languageEnglish
Article number9167450
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number1
Pages (from-to)418-429
Number of pages12
ISSN1524-9050
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Center for Data-Intensive Cyber-Physical Systems (DiCyPS) Project
10.13039/100008398-Obel Family Foundation and the Villum Foundation

Keywords

  • Transportation
  • graph convolutional networks (GCNs)
  • graph representation learning
  • machine learning
  • road network

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