  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.
