Relational Fusion Networks: Graph Convolutional Networks for Road Networks

06/16/2020
by   Tobias Skovgaard Jepsen, et al.
0

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 substantially outperform state-of-the-art GCNs 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.

READ FULL TEXT

page 1

page 3

page 8

page 11

page 12

08/30/2019

Graph Convolutional Networks for Road Networks

Machine learning techniques for road networks hold the potential to faci...
10/23/2018

Finding Appropriate Traffic Regulations via Graph Convolutional Networks

Appropriate traffic regulations, e.g. planned road closure, are importan...
11/14/2019

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network

Road networks are a type of spatial network, where edges may be associat...
11/27/2020

Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles

Rich semantic information extraction plays a vital role on next-generati...
02/03/2020

Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks

Understanding on-road vehicle behaviour from a temporal sequence of sens...
04/11/2019

Relational Graph Attention Networks

We investigate Relational Graph Attention Networks, a class of models th...
10/13/2020

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Supertagging is conventionally regarded as an important task for combina...