Learning Effective Road Network Representation with Hierarchical Graph Neural Networks
Ning Wu, Wayne Xin Zhao, Jingyuan Wang, Dayan Pan
2020年份
109被引次数
17顶会引用
摘要
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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引用它的顶会 Paper17
- Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel SemanticsJiawei Jiang, Dayan Pan, Houxing Ren, Xiaohan Jiang 等ICDE 2023 · 被引用 101 次
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- Road Network Representation Learning with the Third Law of GeographyHaicang Zhou, Weiming Huang, Yile Chen, Tiantian He 等NeurIPS 2024 · 被引用 23 次
- Spatial Heterophily Aware Graph Neural NetworksCongxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu 等KDD 2023 · 被引用 16 次
- Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation LearningChengkai Han, Jingyuan Wang, Yongyao Wang, Xie Yu 等AAAI 2025 · 被引用 16 次
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