Cycling behaviour in Germany: A dataset of cycling parameter across 100 cities based on City Cycling GPS trajectories

The dataset presented was created as part of the cycling campaign City Cycling in Germany during the years 2022-2024. The campaign involved >3000 municipalities across the entire country. Over a three-week period, which participants could freely select between May 1 and September 30, the goal was to replace as many car trips as possible with bicycle trips. Participants could use a smartphone application to record their data. The recorded GPS trajectories were transferred from the smartphone to a data lake as a database dump and were then regularly retrieved by a backend server for data pre-processing. During this process, trips, activities, modes of transport, and driving modes were identified. For the dataset presented, individual trajectories were processed with respect to several core variables characterizing cycling behaviour. The dataset was spatially filtered beforehand. A total of 109 cities and municipalities were selected based on variables such as population size, the number of campaign participants, topography, the modal share of cycling, and geographic location within Germany, in order to obtain a representative impression of cycling behaviour. As a result, >8 million trip records with 37 distinct variables were collected over the data years 2022-2024.The dataset was analysed within the scope of the project Bicycle Driving Behaviour in Germany, focusing on waiting and loss times for cyclists and the influence of various personal and city-specific parameters. Additionally, the data allows for evaluations of cycling frequency, trip distances, speeds, accelerations, daily and weekly patterns, and comparisons between the different data years regarding the influence of weather conditions. Moreover, the known starting and ending points of trips can be used in transport demand modelling for model development and calibration. Overall, the dataset offers extensive opportunities for scientific analysis due to its scope and wide spatial coverage, making it a valuable resource for research in cycling behaviour and transportation planning.