Abstract
This dataset contains high-resolution bicycle trajectory data collected during a controlled mass-cycling experiment conducted on a circular test track at ETH Zurich. A total of 28 cyclists were recorded using aerial video over approximately 30 minutes. The experiment systematically varied the number of simultaneous cyclists and the effective lane width to capture a range of traffic density conditions, including free-flow, disturbed flow, and stop-and-go regimes. Bicycle positions were extracted from drone footage using computer vision-based object detection and tracking, followed by state estimation and Kalman filtering to obtain smooth Cartesian trajectories at frame level. The dataset includes raw video recordings, object annotations, and processed trajectories with spatial and temporal attributes. By isolating cyclist interactions from complex road geometry and mixed traffic, the dataset provides a controlled basis for studying bicycle traffic flow, lateral movement, overtaking manoeuvres, and collective dynamics. The dataset is suitable for use in traffic flow analysis, microscopic modelling, and the development and evaluation of bicycle-specific trajectory prediction methods.
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Acknowledgements
We would like to thank Dr. Catherine Elliot and the other organisers of the Cycling Research Board Annual Meeting (CRB2024) and the organisation Pro Velo Kanton Zurich for promoting the mass-cycling experiment amongst all the experiment participants. Additionally, we would like to thank Judith van den Hoeven for facilitating the coordination with participants, and Veloplan GmbH for the provision of bikes. Moreover, we thank the following supporters who facilitated the experiment: Linghang Sun, Kimia Chavoshi, Yifan Zhang, Qishen Zhou.We promote the principles of open and reproducible science. In line with this commitment, our work aligns with the goals of the Reproducible Research in Transportation (RERITE) Working Group, which advocates transparency, reproducibility, and data sharing in transportation research. Further information about the working group is available at https://www.rerite.org/. Research was supported by the “BikeZ: Model Suite for Mass Cycling as a Service Simulation” Innovation project funded by SERI / Innosuisse (grant agreement: 123.077 IP-SBM).
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Open access funding provided by Swiss Federal Institute of Technology Zurich.
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Riehl, K., El-Baklish, S.K., Ni, YC. et al. BikeZ-ETH – A Mass-Cycling Trajectory Dataset from a Controlled Experiment. Sci Data (2026). https://doi.org/10.1038/s41597-026-07247-7
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DOI: https://doi.org/10.1038/s41597-026-07247-7
