The Python package linkbikenet finds links between the disconnected components in a city's bicycle network. You can download street and bike network data with a single line of code, simulate different bicycle network linking scenarios, and export and plot the resulting prioritized linking steps.

LinkBikeNet is a decision support tool for urban planners. 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.

When to use

LinkBikeNet works well for cities that have some bicycle infrastructure in the form of disconnected components. This is the case for most cities in Europe. Recommended example cities to link components: Budapest, Dublin, Tirana

For alternative approaches, consider using GrowBikeNet or FixBikeNet.

Installation

The easy way

The currently recommended way to install LinkBikeNet is using conda:

conda install linkbikenet

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/LinkBikeNet/

Origin

The source code builds on the code from the research paper Data-driven strategies for optimal bicycle network growth.

Publication: https://doi.org/10.1098/rsos.201130

How to cite

If you use LinkBikeNet, please cite the paper:

L.G. Natera Orozco, F. Battiston, G. Iñiguez, M. Szell. Data-driven strategies for optimal bicycle network growth. Royal Society Open Science 7:201130 (2020) DOI: 10.1098/rsos.201130

Supported by

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

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