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Learning Effective Road Network Representation with Hierarchical Graph Neural Networks
101 Citations•2020•
Ning Wu, Xin Zhao, Jingyuan Wang
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This paper proposes a novel Hierarchical Road Network Representation model, named HRNR, by constructing a three-level neural architecture, corresponding to "functional zone", "structural regions" and "road segments", respectively, and designs aThree-level hierarchical update mechanism for learning the node embeddings through the entire network.
Abstract
Road network is the core component of urban transportation, and it is widely useful in various traffic-related systems and applications. Due to its important role, it is essential to develop general, effective, and robust road network representation models. Although several efforts have been made in this direction, they cannot fully capture the complex characteristics of road networks.