mlsanityData to 7 Oct 2026

Road Planning for Slums via Deep Reinforcement Learning

arXiv 2305.13060 ↗First posted 22 May 2023cs.AI cs.LGcited by 7

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Abstract (arXiv)

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 35 of its 78 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

35 of 78 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.

  1. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  2. 2024Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priorscited by 7
  3. 2025USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agentscited by 5
  4. 2024Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learningcited by 2
  5. 2026TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generationcited by 2
  6. 2024Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learningcited by 0
  7. 2026DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learningcited by 0