  Road network is a critical infrastructure powering many applications
including transportation, mobility and logistics in real life. To leverage the
input of a road network across these different applications, it is necessary to
learn the representations of the roads in the form of vectors, which is named
\emph{road network representation learning} (RNRL). While several models have
been proposed for RNRL, they capture the pairwise relationships/connections
among roads only (i.e., as a simple graph), and fail to capture among roads the
high-order relationships (e.g., those roads that jointly form a local region
usually have similar features such as speed limit) and long-range relationships
(e.g., some roads that are far apart may have similar semantics such as being
roads in residential areas). Motivated by this, we propose to construct a
\emph{hypergraph}, where each hyperedge corresponds to a set of multiple roads
forming a region. The constructed hypergraph would naturally capture the
high-order relationships among roads with hyperedges. We then allow information
propagation via both the edges in the simple graph and the hyperedges in the
hypergraph in a graph neural network context. The graph reconstruction and
hypergraph reconstruction tasks are conventional ones and can capture
structural information. The hyperedge classification task can capture
long-range relationships between pairs of roads that belong to hyperedges with
the same label. We call the resulting model \emph{HyperRoad}. We further extend
HyperRoad to problem settings when additional inputs of road attributes and/or
trajectories that are generated on the roads are available.
