# MetroGNN: Metro Network Expansion with Reinforcement Learning

arXiv: https://arxiv.org/abs/2403.09197 · HTML: https://mlsanity.com/paper/2403.09197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning](https://mlsanity.com/paper/2606.04167/) (2026)

## Abstract (arXiv)

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.