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Sensor Bike

Citizen science project to collect bicycle traffic data.

Participants of the sensor workshop grouped around a table while the speaker explains the sensors.
At the sensor workshop in May 2025, participants were able to build their own bike sensor and mount it on their bike.

Together with 30 frequent cyclists from our city, the state capital of Wiesbaden collected relevant data on bicycle traffic in Wiesbaden throughout 2025. The data experiment was initially scheduled to last six months. 

The multifunctional sensors collected environmental data, recorded the surface conditions (cobblestones, asphalt, etc.) and the likelihood that overtaking vehicles did or did not maintain the minimum passing distance of 1.50 meters required by traffic regulations. All data from the cyclists was aggregated and visualized on a map. 

The campaign data and further information about the data remain available in the dashboard “Data World: Bicycle Use in Wiesbaden.”

Committed residents of our city contributed to this project by collecting data during their everyday bike rides. Professors and students from Rhein-Main University of Applied Sciences also supported the project. Together with the project’s supporters and the City of Wiesbaden’s Bicycle Office, the data will be reviewed and analyzed collaboratively.

The citizen science project was launched during the “fahrRAD” Smart City Theme Week (May 2025) and ran through November 2025.

Smart City draws initial findings from the data

Colorful display of busy and less busy routes in the Wiesbaden city area during the data experiment.
Coverage of the collected data in the Wiesbaden city area.

The available experimental data on cycling in Wiesbaden provides a wide range of insights and enables differentiated analyses that would hardly be possible with stationary counting stations alone:


  • Overtaking distances: The data provides initial indications of dangerous situations across the city, which can then be analyzed in greater depth using additional parameters and information.
  • Surface conditions: The sensor data reveals where cycle paths are particularly comfortable or in need of renovation, providing precise information for infrastructure planning.
  • Routes: The combination of individual routes with sensor data makes it clear which routes are preferred and which are avoided.

Conclusion: Experimental data also contributes to the objectification of discussions and the measurability of qualitative arguments. The data that we have collected together can help us to make good decisions and promote cycling in Wiesbaden. They also help to adapt the cycling infrastructure in Wiesbaden to the needs of cyclists.

The advantages:

  • The project provided valuable data that did not previously exist in this form.
  • The data experiment provided indications and clues that can support the targeted expansion of cycling infrastructure and an improvement in road safety.
  • With a data basis that covers the entire urban area, urgent measures can be prioritized.

Who benefits from it?

  • All road users benefit from improved cycling infrastructure, whether they are cycling, driving or walking.
  • Vulnerable groups in particular (such as children and young people or older people) can move more safely in traffic thanks to an improved cycling infrastructure.

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