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Related Experiment Video

Updated: Jun 29, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

BikeZ-ETH - A Mass-Cycling Trajectory Dataset from a Controlled Experiment.

Kevin Riehl¹, Shaimaa K El-Baklish², Ying-Chuan Ni²

¹Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini Platz 5, 8093, Zurich, Switzerland. kriehl@ethz.ch.

Scientific Data

|April 23, 2026

Summary

This dataset captures detailed bicycle trajectory data from a controlled experiment, offering insights into cyclist interactions and traffic flow dynamics under varying densities. It aids in developing advanced bicycle traffic analysis and prediction models.

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Area of Science:

Transportation Science
Traffic Engineering
Data Science

Background:

Understanding bicycle traffic flow is crucial for urban planning and safety.
Existing datasets often lack controlled conditions for isolating cyclist interactions.
Mass-cycling events present complex dynamics not fully captured by current models.

Purpose of the Study:

To present a high-resolution dataset of bicycle trajectories from a controlled mass-cycling experiment.
To provide a basis for studying bicycle traffic flow, collective dynamics, and interactions.
To facilitate the development and validation of bicycle trajectory prediction methods.

Main Methods:

Collected aerial video data of 28 cyclists on a circular test track.
Utilized computer vision for object detection and tracking of cyclists.
Applied state estimation and Kalman filtering for precise trajectory reconstruction.

Main Results:

Generated smooth Cartesian trajectories at frame level for individual cyclists.
Captured diverse traffic conditions, from free-flow to stop-and-go regimes.
Dataset includes raw video, object annotations, and processed trajectory data.

Conclusions:

The dataset enables isolated analysis of cyclist behavior and interactions.
It serves as a valuable resource for traffic flow analysis and microscopic modeling.
Facilitates research into bicycle-specific trajectory prediction and collective dynamics.