Supplementary materials for my rC3 2021 contribution “Optimising public transport: A data-driven bike-sharing study in Marburg”.

Interactive versions of the Nextbike transition matrix figure from previous blog articles.

In this article, I use machine learning to predict the number of parked bikes in two ways.

I use quantitative analyses to derive social and environmental benefits of the Nextbike system in Marburg. Also, I investigate which routes in Marburg are popular among Nextbike users. Hence, I offer quantitative arguments as to why bikes are good for Marburg and how to improve the biking experience in Marburg even further.

In this article, I draw quantitative conclusions for cyclists who use Nextbikes in Marburg. For these quantitative conclusions, I use Nextbike data that I previously scraped.

I collected Nextbike data in Marburg and introduce my plan to evaluate the data in this article. Also, I present what the data is made up of and how it is obtained in detail. Lastly, a first temporal analysis of the bike usage in Marburg is presented.