MetroGNN: Metro Network Expansion with Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910367146311680 |
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| author | Su, Hongyuan Zheng, Yu Ding, Jingtao Jin, Depeng Li, Yong |
| author_facet | Su, Hongyuan Zheng, Yu Ding, Jingtao Jin, Depeng Li, Yong |
| contents | Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-graph. Our approach employs an attentive policy network that intelligently selects nodes based on information captured by a graph neural network. Experiments on real-world urban data demonstrate that our proposed methodology substantially improve the satisfied transportation demands by over 30\% when compared with state-of-the-art methods. Codes are published at https://github.com/tsinghua-fib-lab/MetroGNN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09197 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MetroGNN: Metro Network Expansion with Reinforcement Learning Su, Hongyuan Zheng, Yu Ding, Jingtao Jin, Depeng Li, Yong Computers and Society 68T09 Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-graph. Our approach employs an attentive policy network that intelligently selects nodes based on information captured by a graph neural network. Experiments on real-world urban data demonstrate that our proposed methodology substantially improve the satisfied transportation demands by over 30\% when compared with state-of-the-art methods. Codes are published at https://github.com/tsinghua-fib-lab/MetroGNN. |
| title | MetroGNN: Metro Network Expansion with Reinforcement Learning |
| topic | Computers and Society 68T09 |
| url | https://arxiv.org/abs/2403.09197 |