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.

A quantitative update of the parking demand in Marburg during the Corona pandemic 2020.

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.

The parking demand in Marburg is analysed quantiatively based on publicly available data. In addition, Gaussian Processes are used for spatial predictions.