Volunteered mass cycling self-tracking data – grade of representation and aptitude for planning

ABSTRACT

Until recently, bicycles have been neglected as an equitable mode of transport in urban traffic. Promoting bicycle traffic, however, is challenging since capturing the diverse behaviour of cyclists is quite difficult. Traditionally, information was point-based (traffic counting) or asked for cost-intensive and time-consuming surveys. GPS data and the popularity of digital applications are increasingly used to capture people’s movement data. Thus the question arises if such data could supplement or even replace conventional methods. About 42,354 trajectories from a Vienna dataset were analysed for how representative they are, which new information they offer and whether and to what extent the data may be used for future transportation planning. The results indicate a strong correlation between GPS-recorded and counted bicycle volumes (R² = up to 0.95). Due to the very restricted grade of representation of 0.032–0.25%, the GPS data can create additional value but cannot replace conventional methods.

KEYWORDS:

• Volunteered data
• cycling planning parameters
• GPS tracking
• traffic volume
• speed

Acknowledgements

The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme. The authors thank Bike Citizens App GmbH for supplying the dataset. They also thank AustriaTech – Gesellschaft des Bundes für technologiepolitische Maßnahmen GmbH for making the national survey raw data available. They also thank four anonymous reviewers for their valuable comments that helped to finalise the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Due to a limited data usage agreement with Bike Citizens App GmbH the data cannot be stored in a publicly available form.

Notes

1 Provision of raw dataset by AustriaTech, Raimundgasse 1/6, 1020 Vienna, Austria.