activity
20192021
most citedRelational Fusion Networks: Graph Convolutional Networks for Road Networks

66 citations · 91 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2021

UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation

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

Travel time or speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or aggregation and represen…

cs.RO2020

Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining

Florian Barth, Stefan Funke, Tobias Skovgaard Jepsen +1

We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and sp…

cs.LG202066 cited

Relational Fusion Networks: Graph Convolutional Networks for Road Networks

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

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convo…

cs.LG201925 cited

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

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

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often…

cs.LG2019

Graph Convolutional Networks for Road Networks

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

Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks t…