Graph Convolutional Networks for Road Networks

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

2 Downloads (Pure)

Resumé

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important 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 pro- pose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.
OriginalsprogEngelsk
TitelProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Antal sider4
Publikationsdato5 nov. 2019
Sider460-463
DOI
StatusUdgivet - 5 nov. 2019
Begivenhed27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - 350 W Mart Center Dr, Chicago, USA
Varighed: 5 nov. 20198 nov. 2019
https://sigspatial2019.sigspatial.org/

Konference

Konference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Lokation350 W Mart Center Dr
LandUSA
ByChicago
Periode05/11/201908/11/2019
Internetadresse

Fingerprint

Learning systems
Fusion reactions
Neural networks

Citer dette

Skovgaard Jepsen, T., Jensen, C. S., & Nielsen, T. D. (2019). Graph Convolutional Networks for Road Networks. I Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (s. 460-463) https://doi.org/10.1145/3347146.3359094
Skovgaard Jepsen, Tobias ; Jensen, Christian S. ; Nielsen, Thomas Dyhre. / Graph Convolutional Networks for Road Networks. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019. s. 460-463
@inproceedings{e8a8f3050a8546e9904fea23d72159f5,
title = "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 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 pro- pose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.",
keywords = "Road Network, Machine Learning, Graph Representation Learning, Graph Convolutional Networks, Transportation",
author = "{Skovgaard Jepsen}, Tobias and Jensen, {Christian S.} and Nielsen, {Thomas Dyhre}",
year = "2019",
month = "11",
day = "5",
doi = "10.1145/3347146.3359094",
language = "English",
pages = "460--463",
booktitle = "Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems",

}

Skovgaard Jepsen, T, Jensen, CS & Nielsen, TD 2019, Graph Convolutional Networks for Road Networks. i Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. s. 460-463, Chicago, USA, 05/11/2019. https://doi.org/10.1145/3347146.3359094

Graph Convolutional Networks for Road Networks. / Skovgaard Jepsen, Tobias; Jensen, Christian S.; Nielsen, Thomas Dyhre.

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019. s. 460-463.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Graph Convolutional Networks for Road Networks

AU - Skovgaard Jepsen, Tobias

AU - Jensen, Christian S.

AU - Nielsen, Thomas Dyhre

PY - 2019/11/5

Y1 - 2019/11/5

N2 - The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important 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 pro- pose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.

AB - The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important 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 pro- pose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.

KW - Road Network

KW - Machine Learning

KW - Graph Representation Learning

KW - Graph Convolutional Networks

KW - Transportation

U2 - 10.1145/3347146.3359094

DO - 10.1145/3347146.3359094

M3 - Article in proceeding

SP - 460

EP - 463

BT - Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

ER -

Skovgaard Jepsen T, Jensen CS, Nielsen TD. Graph Convolutional Networks for Road Networks. I Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019. s. 460-463 https://doi.org/10.1145/3347146.3359094