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Spatial planning of urban communities via deep reinforcement learning

Nature Computational Science volume 3, pages 748–762 (2023)Cite this article

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Abstract

Effective spatial planning of urban communities plays a critical role in the sustainable development of cities. Despite the convenience brought by geographic information systems and computer-aided design, determining the layout of land use and roads still heavily relies on human experts. Here we propose an artificial intelligence urban-planning model to generate spatial plans for urban communities. To overcome the difficulty of diverse and irregular urban geography, we construct a graph to describe the topology of cities in arbitrary forms and formulate urban planning as a sequential decision-making problem on the graph. To tackle the challenge of the vast solution space, we develop a reinforcement learning model based on graph neural networks. Experiments on both synthetic and real-world communities demonstrate that our computational model outperforms plans designed by human experts in objective metrics and that it can generate spatial plans responding to different circumstances and needs. We also propose a human–artificial intelligence collaborative workflow of urban planning, in which human designers can substantially benefit from our model to be more productive, generating more efficient spatial plans with much less time. Our method demonstrates the great potential of computational urban planning and paves the way for more explorations in leveraging computational methodologies to solve challenging real-world problems in urban science.

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Fig. 1: Demonstration of community renovation and a 15-minute city.
Fig. 2: Demonstration of transferring pretrained models to different spatial planning tasks.
Fig. 3: Demonstration of community plans of different styles.
Fig. 4: Comparison and collaboration with professional human designers.

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Data availability

The geographic data of communities used in this work is collected from OpenStreetMap (https://www.openstreetmap.org) using Python 3.8.0 with packages osmnx>=1.1.2 and geopandas>=0.11.1. We provide the data for the three adopted communities in our experiments. The data supporting the results of this study is available on Zenodo (https://doi.org/10.5281/zenodo.8175420)50 and GitHub (https://github.com/tsinghua-fib-lab/DRL-urban-planning). Source data are provided with this paper.

Code availability

The code used in this research can be found at Zenodo (https://doi.org/10.5281/zenodo.8175420)50 and Github (https://github.com/tsinghua-fib-lab/DRL-urban-planning).

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under grant 2020AAA0106000 to Y.L. and the National Natural Science Foundation of China under U1936217, U20B2060 and 61971267 to Y.L. This work was supported in part by Tsinghua-Toyota Joint Research Institute Inter-disciplinary Program to L.Z. and Tsinghua-Cambridge Joint Research Initiative Fund to L.Z. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We are also grateful for the insightful discussion with Y. Yuan and S. Zheng at Tsinghua University.

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Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China

    Yu Zheng, Yuming Lin, Depeng Jin & Yong Li

  2. Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China

    Yu Zheng, Yuming Lin, Depeng Jin & Yong Li

  3. Department of Urban Planning and Design, Tsinghua University, Beijing, P. R. China

    Liang Zhao & Tinghai Wu

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  1. Yu Zheng
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Contributions

Y.Z., L.Z. and Y. Li jointly launched this research. Y.Z., Y. Lin, L.Z. and Y. Li contributed ideas. Y.Z., Y. Lin and Y. Li designed the research methods and provided the research outline. Y.Z. developed the DRL framework. Y.Z. and Y. Lin performed the experiments. Y. Lin, L.Z., T.W., D.J. and Y. Li provided critical revisions. D.J. and Y. Li managed the project. All authors jointly analyzed the results and participated in the writing of the manuscript.

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Correspondence to Yong Li.

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Nature Computational Science thanks Paolo Santi, Weinan Zhang, Thomas W. Sanchez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Problem formulation of community-level spatial planning.

The community-level spatial planning is formulated as a two-stage sequential decision-making problem. In the first stage of land use planning, the agent places different functionalities and open spaces until all the planning needs are satisfied. A reward rL regarding the layout efficiency of all the land use as a whole is returned after the entire community is filled. The result of land use planning serves as the initial conditions of the second stage of road planning, where one land use boundary is selected at each step and planned as a road segment. After a predefined termination step, a reward rR considering the efficiency of road transportation is returned, and all intermediate steps have a reward of 0. Please refer to Supplementary Table 1 for the meaning of different colors.

Extended Data Fig. 2 Reformulation of community spatial planning with a contiguity graph.

a, Land use planning. A graph is constructed based on the contiguity relationship between urban geographical elements. Nodes consist of three different categories, including land (L), segment (S), and junction (J). Land can be vacant land to be planned or already planned land use. Segment can be a road or a land use boundary. Junction is the intersection between roads and land use boundaries. Two nodes are connected with an edge if the underlying geographical elements touch each other. There are five categories of edges in total, including L-L, L-S, L-J, S-S, and S-J. In the stage of land use planning, the agent selects one L-J edge that decides the location to place a given land use type. In the figure as an example, we select the edge between L2 and J4, thus a block in L2 and near J4 is sliced and assigned as a newly planned land use type. After each step, new urban geographical elements are added and contiguity relationships change, thus a different graph with a distinct topology will be constructed at the next step. b, Road planning. Graph in the road planning task is defined in the same way as the land use planning task. At each step, the agent selects one S node that is currently a land use boundary, and plans a road at its location. In the figure, for example, we select S10, which is the boundary of L2 and L4 from three feasible candidates (S9, S10, and S12), and replace it with a road segment. In the next step, the node attribute of S10 changes, that is, its type changes from boundary to road, and two feasible candidates are left (S9 and S12).

Extended Data Fig. 3 Overview of the proposed framework.

Our framework consists of two separate policy networks (b and d) that take actions for the land use planning task (e) and road planning task (f) respectively, and share a GNN state encoder (a) with a value network (c) that estimates the effect of the current planning result. a, A GNN-based state encoder takes the contiguity graph states as input and learns representations of nodes, edges, and the whole graph. b, A land use policy network utilizes the obtained edge embeddings and scores each edge with a MLP for edge selection. d, Similarly, a road policy network scores each node from the obtained node embeddings. c, A value network containing one fully-connected layer predicts the performance of the current spatial plan with the graph embedding. e, The spatial planning environment receives actions on the contiguity graph, places land use or road accordingly in actual geographical space, and transforms it into states in a graph form.

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Zheng, Y., Lin, Y., Zhao, L. et al. Spatial planning of urban communities via deep reinforcement learning. Nat Comput Sci 3, 748–762 (2023). https://doi.org/10.1038/s43588-023-00503-5

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