DRL urban planning

In this project, we propose a reinforcement-learning-based framework for assisting urban planners in the complex task of optimizing the spatial design of urban communities. Our proposed model can generate land and road layout with superior spatial efficiency, and improve the productivity of human planners with a human-AI collaborative workflow.

This project was initially described in the research article in Nature Computational Science:

Yu Zheng, Yuming Lin, Liang Zhao, Tinghai Wu, Depeng Jin, Yong Li. Spatial planning of urban communities via deep reinforcement learning. Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00503-5

Full text (PDF) is available at this link.

Installation

Environment

Tested OS: Linux
Python >= 3.8
PyTorch >= 1.8.1, <= 1.13.0

Dependencies:

Install PyTorch with the correct CUDA version.
Set the following environment variable to avoid problems with multiprocess trajectory sampling:
```
export OMP_NUM_THREADS=1
```

Data

The data used for training and evaluation can be found in urban_planning/cfg/test_data. We provide all the three scenarios used in our paper, including one synthetic grid community in urban_planning/cfg/test_data/synthetic, and two real-world communities, HLG and DHM, with and without planning concepts, in urban_planning/cfg/test_data/real. The data for the real-world communities are collected from the widely used OpenStreetMap (OSM) using OSMnx. For each case, we provide the following data:

init_plan.pickle: the initial conditions of the community in geopandas.GeoDataFrame form, including the initial land blocks, roads, and junctions.
objectives.yaml: the planning objectives (requirements) of the community in yaml form, including the required number/area of different functionalities, and the minimum/maximum area of each land use type.

The figure below illustrates the initial conditions of the three scenarios.

With the initial conditions and planning objectives, the agent will generate millions of spatial plans for the community in real-time during training, which are stored in the replay buffer for training.

Training

You can train your own models using the provided config in urban_planning/cfg/exp_cfg/real.

For example, to train a model for the HLG community, run:

python3 -m urban_planning.train --cfg hlg --global_seed 111

You can replace hlg to dhm to train for the DHM community.

To train a model with planning concepts for the HLG community, run:

python3 -m urban_planning.train --cfg hlg_concept --global_seed 111

You can replace hlg_concept to dhm_concept to train for the DHM community.

You will see the following logs once you start training our model:

running_code.mp4

Visualization

You can visualize the generated spatial plans using the provided notebook in demo.

Baselines

To evaluate the centralized heuristic, run:

python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent rule-centralized

To evaluate the decentralized heuristic, run:

python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent rule-decentralized

To evaluate the geometric set-coverage adapted baseline, run:

python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent gsca

To evaluate the GA baseline, run:

python3 -m urban_planning.train_ga --cfg hlg --global_seed 111
python3 -m urban_planning.eval --cfg hlg --global_seed 111 --agent ga

You can replace hlg to dhm to evaluate for the DHM community.

Final plans

We provide the final plans (geojson format) generated by our model for the HLG and DHM communities in results.

Change base map

We conduct experiments on the HLG and DHM communities, which are real-world communities in Beijing, China. You can change the base map to your own community. We provide an example in extra showing how to prepare the base map data of another community in Huizhou, China.

Citation

If you use this code in your project, please consider citing the following paper:

@article{zheng_spatial_2023,
	title = {Spatial planning of urban communities via deep reinforcement learning},
	volume = {3},
	issn = {2662-8457},
	url = {https://doi.org/10.1038/s43588-023-00503-5},
	doi = {10.1038/s43588-023-00503-5},
	number = {9},
	journal = {Nature Computational Science},
	author = {Zheng, Yu and Lin, Yuming and Zhao, Liang and Wu, Tinghai and Jin, Depeng and Li, Yong},
	month = sep,
	year = {2023},
	pages = {748--762},
}

License

Please see the license for further details.

Note

The implemention is based on Transform2Act and circuit_training.

Awesome AI Planning Support Tools

With urban planning being a long-standing problem, researchers have devoted decades of efforts to developing computational models and supporting tools for it in order to automate its process. Automated spatial layout seemed impossible until much more recently with the latest advancements in artificial intelligence, especially deep reinforcement learning. In fact, our proposed DRL approach is inspired by, and takes a small step forward from, the planning support tools that have been available for the past few decades. Here, we summarize these existing awesome planning support tools that utilize AI to facilitate urban planning.

Land use planning

paper	keywords	venue	year
Wang, D., Fu, Y., Wang, P., Huang, B., & Lu, C. T. (2020, November). Reimagining city configuration: Automated urban planning via adversarial learning. In Proceedings of the 28th international conference on advances in geographic information systems(pp. 497-506).	land-use configuration, GAN	SIGSPATIAL	2020
Wang, D., Fu, Y., Liu, K., Chen, F., Wang, P., & Lu, C. T. (2023). Automated urban planning for reimagining city configuration via adversarial learning: quantification, generation, and evaluation.ACM Transactions on Spatial Algorithms and Systems,9(1), 1-24.	land-use configuration, GAN	ACM Transactions on Spatial Algorithms and Systems	2023
Wang, D., Wu, L., Zhang, D., Zhou, J., Sun, L., & Fu, Y. (2023, June). Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 4660-4667).	land-use configuration, Transformer	AAAI	2023
Yao, J., Murray, A. T., Wang, J., & Zhang, X. (2019). Evaluation and development of sustainable urban land use plans through spatial optimization.Transactions in GIS,23(4), 705-725.	land use plan	Transactions in GIS	2019
Dahal, K. R., & Chow, T. E. (2014). A GIS toolset for automated partitioning of urban lands.Environmental Modelling & Software,55, 222-234.	land use partition, ArcGIS toolset	Environmental Modelling & Software	2014
Ligmann‐Zielinska, A., Church, R. L., & Jankowski, P. (2008). Spatial optimization as a generative technique for sustainable multiobjective land‐use allocation. International Journal of Geographical Information Science, 22(6), 601-622.	land-use allocation, optimization	International Journal of Geographical Information Science	2008
Xu, Y., Olmos, L. E., Abbar, S., & González, M. C. (2020). Deconstructing laws of accessibility and facility distribution in cities.Science advances,6(37), eabb4112.	facility location, swap-based local search	Science Advances	2020
Merrell, P., Schkufza, E., & Koltun, V. (2010). Computer-generated residential building layouts. InACM SIGGRAPH Asia 2010 papers(pp. 1-12).	building layout,Bayesian network	ACM SIGGRAPH Asia	2010
Liu, Y., Luo, Y., Deng, Q., & Zhou, X. (2021). Exploration of Campus Layout Based on Generative Adversarial Network: Discussing the Significance of Small Amount Sample Learning for Architecture. In Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) (pp. 169-178). Springer Singapore.	campus layout, GAN	Digital Futures	2021
Tian, R. (2021). Suggestive site planning with conditional gan and urban gis data. In Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) (pp. 103-113). Springer Singapore.	site planning, GAN	Digital Futures	2020

Road planning

paper	keywords	venue	year
Zheng, Y., Su, H., Ding, J., Jin, D., & Li, Y. (2023). Road Planning for Slums via Deep Reinforcement Learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5695–5706).	road network generation, DRL	KDD	2023
Xue, J., Jiang, N., Liang, S., Pang, Q., Yabe, T., Ukkusuri, S. V., & Ma, J. (2022). Quantifying the spatial homogeneity of urban road networks via graph neural networks. Nature Machine Intelligence, 4(3), 246-257.	road network analysis, GNN	Nature Machine Intelligence	2022
Fang, Z., Jin, Y., & Yang, T. (2022). Incorporating planning intelligence into deep learning: A planning support tool for street network design.Journal of Urban Technology,29(2), 99-114.	street network generation, GAN	Journal of Urban Technology	2022
Fang, Z., Qi, J., Fan, L., Huang, J., Jin, Y., & Yang, T. (2022). A topography-aware approach to the automatic generation of urban road networks.International Journal of Geographical Information Science,36(10), 2035-2059.	road network generation, GAN	International Journal of Geographical Information Science	2022
Li, J., Lyu, L., Shi, J., Zhao, J., Xu, J., Gao, J., ... & Sun, Z. (2022, November). Generating community road network from GPS trajectories via style transfer. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems (pp. 1-4).	road network generation, Style Transfer	SIGSPATIAL	2022

Urban design

paper	keywords	venue	year
Zheng, H., & Yuan, P. F. (2021). A generative architectural and urban design method through artificial neural networks. Building and Environment, 205, 108178.	urban design, ANN	Building and Environment	2021
Sun, Y., & Dogan, T. (2023). Generative methods for Urban design and rapid solution space exploration.Environment and Planning B: Urban Analytics and City Science,50(6), 1577-1590.	urban design, tensor field	Environment and Planning B: Urban Analytics and City Science	2023
Duering, S., Chronic, A., & Koenig, R. (2020, May). Optimizing Urban Systems: Integrated optimization of spatial configurations. In Proceedings of the 11th annual symposium on simulation for architecture and urban design (pp. 1-7).	urban design, evolutionary optimization	SimAUD	2020
Han, Z., Yan, W., & Liu, G. (2021). A performance-based urban block generative design using deep reinforcement learning and computer vision. InProceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)(pp. 134-143). Springer Singapore.	urban block design, deep RL, CV	Digital Futures	2020
Fedorova, S. (2021). Generative adversarial networks for urban block design. InSimAUD 2021: A Symposium on Simulation for Architecture and Urban Design.	urban block design, GAN	SimAUD	2021
Koenig, R., Miao, Y., Aichinger, A., Knecht, K., & Konieva, K. (2020). Integrating urban analysis, generative design, and evolutionary optimization for solving urban design problems. Environment and Planning B: Urban Analytics and City Science, 47(6), 997-1013.	urban master-design, evolutionary optimization	Environment and Planning B: Urban Analytics and City Science	2019
Ye, X., Du, J., & Ye, Y. (2022). MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks.Environment and Planning B: Urban Analytics and City Science,49(3), 794-814.	urban master-design, GAN	Environment and Planning B: Urban Analytics and City Science	2021
Hua, H., Hovestadt, L., Tang, P., & Li, B. (2019). Integer programming for urban design.European Journal of Operational Research,274(3), 1125-1137.	urban design, integer programming	European Journal of Operational Research	2019
Hidalgo, C. A., Castañer, E., & Sevtsuk, A. (2020). The amenity mix of urban neighborhoods.Habitat International,106, 102205.	urban design, amenities mix, clusters	Habitat International	2020
Shen, J., Liu, C., Ren, Y., & Zheng, H. (2020, August). Machine learning assisted urban filling. InProceedings of the 25th CAADRIA Conference, Bangkok, Thailand(pp. 5-6).	urban filling, GAN	CAADRIA	2020
Galanos, T., Liapis, A., Yannakakis, G. N., & Koenig, R. (2021, July). ARCH-Elites: Quality-diversity for urban design. InProceedings of the Genetic and Evolutionary Computation Conference Companion(pp. 313-314).	urban design, MAP	GECCO	2021
Xu, X., Yin, C., Wang, W., Xu, N., Hong, T., & Li, Q. (2019). Revealing urban morphology and outdoor comfort through genetic algorithm-driven urban block design in dry and hot regions of China.Sustainability,11(13), 3683.	urban block design, genetic algorithm	Sustainability	2019

15-minute city

paper	keywords	venue	year
Vich, G., Gómez-Varo, I., & Marquet, O. (2023). Measuring the 15-Minute City in Barcelona. A geospatial three-method comparison. In Resilient and Sustainable Cities(pp. 39-60). Elsevier.	15-minute city	Resilient and Sustainable Cities	2023
Ferrer-Ortiz, C., Marquet, O., Mojica, L., & Vich, G. (2022). Barcelona under the 15-minute city lens: mapping the accessibility and proximity potential based on pedestrian travel times.Smart Cities,5(1), 146-161.	15-minute city, network analysis	Smart Cities	2022
Allam, Z., Nieuwenhuijsen, M., Chabaud, D., & Moreno, C. (2022). The 15-minute city offers a new framework for sustainability, liveability, and health.The Lancet Planetary Health,6(3), e181-e183.	15-minute city	The Lancet Planetary Health	2022
Allam, Z., Bibri, S. E., Chabaud, D., & Moreno, C. (2022). The ‘15-Minute City’concept can shape a net-zero urban future. Humanities and Social Sciences Communications, 9(1), 1-5.	15-minute city	Humanities and Social Sciences Communications	2022
Noworól, A., Kopyciński, P., Hałat, P., Salamon, J., & Hołuj, A. (2022). The 15-Minute City—The Geographical Proximity of Services in Krakow. Sustainability, 14(12), 7103.	15-minute city, network analysis	Sustainability	2022
Balletto, G., Ladu, M., Milesi, A., & Borruso, G. (2021). A methodological approach on disused public properties in the 15-minute city perspective.Sustainability,13(2), 593.	15-minute city	Sustainability	2021
Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). Introducing the “15-Minute City”: Sustainability, resilience and place identity in future post-pandemic cities. Smart Cities, 4(1), 93-111.	15-minute city	Smart Cities	2021
Weng, M., Ding, N., Li, J., Jin, X., Xiao, H., He, Z., & Su, S. (2019). The 15-minute walkable neighborhoods: Measurement, social inequalities and implications for building healthy communities in urban China. Journal of Transport & Health, 13, 259-273.	15-minute city	Journal of Transport & Health	2019

Review

paper	keywords	venue	year
Lin, B., Jabi, W., Corcoran, P., & Lannon, S. (2023). The application of deep generative models in urban form generation based on topology: a review. Architectural Science Review, 1-16.	review, generative models, urban topology	Architectural Science Review	2023
Miao, Y., Koenig, R., & Knecht, K. (2020, May). The development of optimization methods in generative urban design: a review. InProceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design(pp. 1-8).	review, generative urban design, optimization	SimAUD	2020
Wu, A. N., Stouffs, R., & Biljecki, F. (2022). Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales. Building and Environment, 109477.	review, GAN, built environment	Building and Environment	2023
Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: a review of applications.Landscape Ecology,22, 1447-1459.	review, land-use modeling	Landscape Ecology	2007

Others

paper	keywords	venue	year
Quan, S. J. (2022). Urban-GAN: An artificial intelligence-aided computation system for plural urban design. Environment and Planning B: Urban Analytics and City Science, 49(9), 2500-2515.	public participation, GAN	Environment and Planning B: Urban Analytics and City Science	2022
Wang, X., Song, Y., & Tang, P. (2020). Generative urban design using shape grammar and block morphological analysis. Frontiers of Architectural Research, 9(4), 914-924.	shape grammar, CityEngine	Frontiers of Architectural Research	2020
Rahimian, M., Beirão, J. N., Duarte, J. M. P., & Iulo, L. D. (2019). A Grammar-Based Generative Urban Design Tool Considering Topographic Constraints The Case for American Urban Planning. In37th Conference on Education and Research in Computer Aided Architectural Design in Europe and 23rd Conference of the Iberoamerican Society Digital Graphics, eCAADe SIGraDi 2019(pp. 267-276). Education and research in Computer Aided Architectural Design in Europe.	shape grammar,topological constraint	eCAADe SIGraDi	2019
Huang, C., Zhang, G., Yao, J., Wang, X., Calautit, J. K., Zhao, C., ... & Peng, X. (2022). Accelerated environmental performance-driven urban design with generative adversarial network.Building and Environment,224, 109575.	environmental performance, GAN	Building and Environment	2022
Sun, C., Zhou, Y., & Han, Y. (2022). Automatic generation of architecture facade for historical urban renovation using generative adversarial network.Building and Environment,212, 108781.	historical urban renovation, GAN	Building and Environment	2022
Ligmann-Zielinska, A., & Jankowski, P. (2007). Agent-based models as laboratories for spatially explicit planning policies. Environment and Planning B: Planning and Design, 34(2), 316-335.	urban growth, ABM	Environment and Planning B: Urban Analytics and City Science	2007
Zhang, W., Ma, Y., Zhu, D., Dong, L., & Liu, Y. (2022, August). Metrogan: Simulating urban morphology with generative adversarial network. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2482-2492).	urban morphology, GAN	KDD	2022
Jia, P., Fu, S., Li, Z., & He, H. (2019, December). Low-carbon optimization of spatial pattern in Shenfu New District based on genetic algorithm. In Journal of Physics: Conference Series (Vol. 1419, No. 1, p. 012039). IOP Publishing.	spatial pattern, genetic algorithm, low-carbon	Journal of Physics: Conference Series	2019
Balling, R. J., Taber, J. T., Brown, M. R., & Day, K. (1999). Multiobjective urban planning using genetic algorithm.Journal of urban planning and development,125(2), 86-99.	urban planning, genetic algorithm	Journal of Urban Planning and Development	1999

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README.md

README.md

DRL urban planning

Installation

Environment

Dependencies:

Data

Training

Visualization

Baselines

Final plans

Change base map

Citation

License

Note

Awesome AI Planning Support Tools

Land use planning

Road planning

Urban design

15-minute city

Review

Others

Files

README.md

Latest commit

History

README.md

File metadata and controls

DRL urban planning

Installation

Environment

Dependencies:

Data

Training

Visualization

Baselines

Final plans

Change base map

Citation

License

Note

Awesome AI Planning Support Tools

Land use planning

Road planning

Urban design

15-minute city

Review

Others