The Python package fixbikenet identifies the most important gaps to fill in a city's bicycle network. You can download street and bike network data with a single line of code, simulate different bicycle network fixing scenarios, and export and plot the resulting prioritized gaps.

FixBikeNet is a decision support tool for urban planners. It is also useful for proactive citizens to help inform their city about data-driven improvements, and it aims to foster research on bicycle networks.

When to use

FixBikeNet works well for cities or areas that have a well developed -but not yet perfect- bicycle network. Recommended example cities to fix: Aalborg, Amsterdam, Copenhagen

For alternative approaches, or for cities with less developed bicycle networks, consider using LinkBikeNet or extending the existing network with GrowBikeNet.

Installation

The easy way

The currently recommended way to install FixBikeNet is using conda:

conda install fixbikenet

Advanced and development installations

See our installation docs for details.

Usage

We provide a minimum working example in two formats:

• Python script (examples/mwe.py)
• Jupyter notebook (examples/mwe.ipynb)

Docs

Find more information in our docs: https://docs.bikenetkit.org/FixBikeNet/

Source

The source code builds on the code from the research paper Automated Detection of Missing Links in Bicycle Networks.

Publication: https://doi.org/10.1111/gean.12324

How to cite

If you use FixBikeNet in your research, please cite the paper:

A. Vybornova, T. Cunha, A. Gühnemann, M. Szell. Automated Detection of Missing Links in Bicycle Networks. Geographical Analysis 55(2), 239-267 (2023) DOI: 10.1111/gean.12324

Supported by

Development of BikeNetKit/FixBikeNet is supported by the Innovation Fund Denmark and the EU HORIZON project JUST STREETS.