Guideline: Big Data im Radverkehr

Summary

This guide offers a practical introduction to the use of GPS data for bicycle traffic planning, giving a
comprehensive overview of its opportunities, obstacles and potential.

For cycling to be attractive, the infrastructure must be of high quality. Due to the high level of resources
required to record it locally, the available data on the volume of cycling traffic has to date been patchy. At
the moment, the most reliable and usable numbers seem to be derived from permanently installed automatic
cycling traffic counters, already used by many local authorities. One disadvantage of these is that the number
of data collection points is generally far too low to cover the entirety of a city or other municipality in a way
that achieves truly meaningful results. The effect of side roads on cycling traffic is therefore only incompletely
assessed. Furthermore, there is usually no data at all on other parameters, such as waiting times, route choices
and cyclists’ speed. This gap might in future be filled by methods such as GPS route data, as is now possible by
today’s widespread use of smartphones and the relevant tracking apps. The results of the project presented in
this guide have been supported by the BMVI [Federal Ministry of Transport and Digital Infrastructure] within
the framework of its 2020 National Cycling Plan. This research project seeks to investigate the usability of user
data generated using a smartphone app for bicycle traffic planning by local authorities.

In summary, it can be stated that, taking into account the factors described in this guide, GPS data are usable
for bicycle traffic planning within certain limitations. (The GPS data evaluated in this case were provided by
Strava Inc.) Nowadays it is already possible to assess where, when and how cyclists are moving around across
the entire network. The data generated by the smartphone app could be most useful to local authorities as a
supplement to existing permanent traffic counters. However, there are a few aspects that need to be considered
when evaluating and interpreting the data, such as the rather fitness-oriented context of the routes surveyed in
the examples examined. Moreover, some of the data is still provided as database or GIS files, although some
online templates that are easier to use are being set up, and some can already be used in a basic initial form.
This means that evaluation and interpretation still require specialist expertise as well as human resources.
However, the need for these is expected to reduce in the future with the further development of web interfaces
and supporting evaluation templates. For this to work, developers need to collaborate with local authorities to
work out what parameters are needed as well as the most suitable formats. This research project carried out an
approach to extrapolating cycling traffic volumes from random samples of GPS data over the whole network.
This was also successfully verified in another municipality. Further research is still nevertheless required in the
future, as well as adaptation to the needs of different localities.

Evidence for the usability of GPS data in practice still needs to be acquired in the near future. The cities
of Dresden, Leipzig and Mainz could be taken as examples for this, as they have all already taken their first
steps in the use of GPS data in planning for and supporting cycling. These steps make sense in the light of
the increasing digitisation of traffic and transport and the growing amount of data available as a result –
despite the limitations on these data to date – so that administrative bodies can start early in building up the
appropriate skills among their staff. The use of GPS data would yield benefits for bicycle traffic planning in
the long run. In addition, the active involvement of cyclists opens up new possibilities in communication and
citizen participation – even without requiring specialist knowledge. This guide delivers a practical introduction
to the topic, giving a comprehensive overview of the opportunities, obstacles and potential offered by GPS
data.