  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.
