It’s 17 years since the editor in chief of Wired Magazine published his provocative piece on the end of the scientific method and the new era of data-driven everything [1]. The enthusiasm was great and has kept the (cycling) transport research community on its toes to this day. As more new data sets and advanced computing techniques became available, the transport modelling community, traditionally reliant on vast amounts of data, was particularly optimistic to make big leaps forward [2, 3]. Currently, we see emerging data sources and data-driven applications, which bear huge potentials for the transition towards sustainable cities. With AI-driven solutions on the horizon, the topic gains additional momentum. However, this potential remains largely untapped due to slow adoption in administration and public engagement processes.
While the advancements have significantly contributed to the field, it is essential to critically reassess the impact and methodologies of data-driven cycling research. We plan to tackle the following problem statements in a collaborative setting:
• There is a need to evaluate the effectiveness of data-driven approaches in promoting cycling and fostering sustainable mobility systems, especially in times of a societal and transportation policies backlash in many countries
• The concept of “data triangulation” - combining different datasets to form a comprehensive view - needs further exploration, including methods for semantically and syntactically integrating various datasets.
• Data quality issues and the identification of trustworthy datasets for ground truthing are critical when working with closed data sources. Moreover, we are concerned about the reliance on proprietary data providers and academic principles of freedom, publicness, and transparency. Thus, we must discuss how to pool research data efficiently and grant accessibility across communities.
• The epistemological foundations of data-driven cycling research need clarification, especially in differentiating real-world phenomena from their digital representations and integrating theory-led research with big data analysis.

Literature:
1. Anderson, C., The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, in Wired Magazin. 2008, Condé Nast Publications: San Francisco.
2. Miller, H.J. and S.-L. Shaw, Geographic Information Systems for Transportation in the 21st Century. Geography Compass, 2015. 9(4): p. 180-189.
3. Anda, C., A. Erath, and P.J. Fourie, Transport modelling in the age of big data. International Journal of Urban Sciences, 2017. 21(sup1): p. 19-42.
4. Succi, S. and P.V. Coveney, Big data: the end of the scientific method? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2019. 377(2142): p. 20180145.