Treffer: A decision-making framework based on multi-criteria decision analysis for evaluating virtual bike-sharing station locations with spatial data.

As cities worldwide promote active transport and reduce car dependency, effective planning of bike-sharing systems has become increasingly important for sustainable urban development. Planning effective locations for bike-sharing stations remains a major challenge for sustainable urban mobility, requiring the integration of conflicting criteria and diverse spatial data. While various approaches have been proposed for planning bike-sharing systems, the integration of multi-criteria reasoning with up-to-date, openly available spatial data remains a challenge that is not yet fully addressed. This paper introduces a novel decision-making framework that systematically evaluates virtual bike-sharing station locations by combining the Stable Preference Ordering Towards Ideal Solution (SPOTIS) and RANking COMparison (RANCOM) methods with OpenStreetMap data. The proposed framework is highly flexible and can be adapted to the specific characteristics and needs of a given city or district, allowing decision-makers to select and adjust evaluation criteria according to local priorities and planning contexts. At the same time, it ensures preference stability, enabling consistent and robust rankings even when the set of evaluated locations is modified. In this paper, the framework is demonstrated using five criteria: accessibility to public transport, university area density, cycling infrastructure, points of interest, and residential density, with input from three experts. To demonstrate the applicability of the proposed framework, an evaluation of ten potential locations was conducted in Szczecin. The analysis took into account the preferences of the decision makers and allowed the identification of locations that scored highly in different weighting scenarios. This approach supports the transformation of complex spatial decision-making into systematic, data-informed recommendations, providing urban planners with a tool that can be adapted to various geographical and planning contexts. Thanks to its flexibility and methodological structure, the framework offers practical support for evidence-based planning of bike-sharing infrastructure, helping to connect spatial analysis with real-world urban decision-making. [ABSTRACT FROM AUTHOR]