The Python package growbikenet grows an urban bicycle network from scratch or from an existing bicycle network. You can download street and bike network data with a single line of code, simulate different bicycle network growth scenarios, and export and plot the resulting prioritized growth steps.
GrowBikeNet is a decision support tool for urban planners based on the Dutch CROW Design manual for bicycle traffic. It is also useful for proactive citizens to create a compelling vision for urban cycling in their city, and it aims to foster research on bicycle networks. GrowBikeNet is fully customizable and data-driven allowing to explore different scenarios - for example, you can import custom data sets such as points of interest or limit network development to specific streets to adapt the software to your local needs.
The recommended way to install GrowBikeNet is using conda (or the faster mamba) via the conda-forge channel:
conda install growbikenet -c conda-forge
For more installation options, see our Installation docs.
We provide a minimum working example in two formats:
- Python script (examples/mwe.py)
- Jupyter notebook (examples/mwe.ipynb)
For a walkthrough with illustrative examples, see our Usage docs.
Find more information in our docs: https://docs.bikenetkit.org/GrowBikeNet/
The source code builds on the code from the research paper Growing Urban Bicycle Networks and on the code from the research paper Data-driven micromobility network planning for demand and safety.
Publication (primary): https://doi.org/10.1038/s41598-022-10783-y
Publication (secondary): https://doi.org/10.1177/23998083221135611
If you use GrowBikeNet in your research, please cite the primary paper:
M. Szell, S. Mimar, T. Perlman, G. Ghoshal, R. Sinatra. Growing urban bicycle networks. Scientific Reports 12, 6765 (2022).
DOI: 10.1038/s41598-022-10783-y
Development of BikeNetKit/GrowBikeNet is supported by the Innovation Fund Denmark, the EU HORIZON project JUST STREETS, and the Data Science Section of IT University of Copenhagen.
