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Abstrakt
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.
Originalsprog | Engelsk |
---|---|
Artikelnummer | 9167450 |
Tidsskrift | IEEE Transactions on Intelligent Transportation Systems |
Vol/bind | 23 |
Udgave nummer | 1 |
Sider (fra-til) | 418-429 |
Antal sider | 12 |
ISSN | 1524-9050 |
DOI | |
Status | Udgivet - jan. 2022 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Relational Fusion Networks: Graph Convolutional Networks for Road Networks'. Sammen danner de et unikt fingeraftryk.Projekter
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DiCyPS: Center for Data-Intensive Cyber-Physical Systems
01/01/2015 → 31/12/2020
Projekter: Projekt › Forskning
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Fil2 Citationer (Scopus)5 Downloads (Pure) -
Graph Convolutional Networks for Road Networks
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Åben adgangFil10 Citationer (Scopus)239 Downloads (Pure)