Learning Effective Road Network Representation with Hierarchical Graph Neural Networks
Ning Wu, Wayne Xin Zhao, Jingyuan Wang, Dayan Pan
2020Year
109Citations
17Top-tier citations
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.
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Cited by top-tier papers17
- Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel SemanticsJiawei Jiang, Dayan Pan, Houxing Ren, Xiaohan Jiang et al.ICDE 2023 · 101 citations
- RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal TransformerYuqi Chen, Hanyuan Zhang, Weiwei Sun, Baihua ZhengICDE 2023 · 70 citations
- Road Network Representation Learning with the Third Law of GeographyHaicang Zhou, Weiming Huang, Yile Chen, Tiantian He et al.NeurIPS 2024 · 23 citations
- Spatial Heterophily Aware Graph Neural NetworksCongxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu et al.KDD 2023 · 16 citations
- Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation LearningChengkai Han, Jingyuan Wang, Yongyao Wang, Xie Yu et al.AAAI 2025 · 16 citations
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