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 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. T o 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. Personal use of this material is permitted. Road networks represent an important class of spatial networks and are an essential component of modern societal infrastructure. Road networks are associated with many important analysis tasks such as traffic flow and travel pattern analyses. In particular, many important road network tasks are supported by machine learning algorithms, including travel-time estimation [1], [2], traffic forecasting [3], and k nearest points-of-interest queries [4], [5], that require set of informative features to describe, e.g., the different road segments. Solving road network analysis tasks is difficult since there is often little information available beyond the network structure itself.