The Python package growbikenet grows an urban bicycle network from scratch or from an existing bicycle network.
The software downloads and pre-processes OpenStreetMap or local data, prepares seed points to connect, grows a network, ranks the edges, saves the results, creates plots and videos.
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.
