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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)


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 incomplete; for instance, OpenStreetMap only has speed
limits for 13% of all Danish road segments. This is problematic
for analysis tasks that rely on such information for machine
learning. To enable machine learning in such circumstances, one
may consider the application of network embedding methods to
extract structural information from the network. However, these
methods have so far mostly been used in the context of social
networks, which differ significantly from road networks in terms
of, e.g., node degree and level of homophily (which are key to
the performance of many network embedding methods).
We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in road
networks. Due to the often limited availability of information
on other relevant road characteristics, the analysis focuses on
leveraging the spatial network structure. Our results suggest that
network embedding methods can indeed be used for deriving
relevant network features (that may, e.g, be used for predicting
speed limits), but that the qualities of the embeddings differ from
embeddings for social networks.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE International Conference on Big Data
Number of pages10
Publication date2018
ISBN (Electronic)978-1-5386-5035-6
Publication statusPublished - 2018
Event2018 IEEE International Conference on Big Data - Seattle, United States
Duration: 10 Dec 201813 Dec 2018


Conference2018 IEEE International Conference on Big Data
CountryUnited States


  • road network
  • machine learning
  • feature learning
  • network embedding

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