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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-01, Vol.23 (1), p.418-429
Main Authors: Jepsen, Tobias Skovgaard, Jensen, Christian S., Nielsen, Thomas Dyhre
Format: Article
Language:English
Subjects:
Aggregates
Artificial neural networks
Cognitive tasks
Estimation
Foreign operations of US corporations
graph convolutional networks (GCNs)
graph representation learning
Machine learning
road network
Roads
Task analysis
Transportation
Transportation applications
Transportation networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3011799