Road Network Representation Learning: A Dual Graph based Approach

• URL: http://arxiv.org/abs/2304.07298v1
• Date: Thu, 13 Apr 2023 09:30:11 GMT
• Title: Road Network Representation Learning: A Dual Graph based Approach
• Authors: Liang Zhang and Cheng Long
• Abstract summary: Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. It is necessary to learn the representations of the roads in the form of vectors, which is named emphroad network representation learning (RNRL)
• Score: 15.092888613780406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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