The development and expansion of active mobility is of central interest to cities, districts, and municipalities in Germany. In particular, an attractive travel time and a comfortable travel experience—compared to other modes of transport—are essential. Especially for cycling, waiting times at intersections, along with the associated braking and acceleration processes, are not only physically demanding but also time-consuming. Therefore, minimizing these delays is crucial for ensuring a smooth and pleasant ride. At the same time, adjusted waiting times at traffic signals can help reduce the number of red-light violations.
The poster presentation will illustrate the impact of city level-factors on cyclists' waiting times. To achieve this, trajectory data—particularly waiting times—collected through the annual STADTRADELN climate protection campaign, which takes place in over 3,000 municipalities, will be analysed. First, a precise definition of waiting and lost time at signalised intersections and priority-controlled junctions will be established. Subsequently, waiting clusters within street networks will be identified and examined.
Using data from more than 90 municipalities, the presentation will identify differences in lost time at the municipal level and explore their underlying causes. The municipalities were selected based on various variables, including different municipality sizes (classified by REGIOSTAR), three topographic categories (flat, hilly, mountainous), and the share of cycling in the local transportation mix. Initial descriptive analyses have already revealed significant differences in the average waiting and lost time at intersections and traffic signals.
The analyses presented in the poster will be conducted at the user level and aggregated within municipal boundaries. Using regression models, significant influences on the average waiting time in individual municipalities will be analysed and presented. Independent variables include the presence of cycling infrastructure, the density of traffic signals, subjective perceptions of cycling (from the Bicycle Climate Test), sociodemographic and topographic characteristics, public transportation availability, and other relevant factors.

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.