Enhancing Bicycle Route Selection with Fuzzy Logic: A Case Study in Augsburg

Abstract Current bicycle routing applications often rely on numerical metrics. These offer limited support for users seeking more nuanced, human-readable route assessments. This paper explores the use of a fuzzy inference system to enhance route selection. The idea is to model subjective and qualitative characteristics with fuzzy logic. By comparing routes from a set of alternatives for a given origin–destination pair, we demonstrate how fuzzy set theory can translate complex quantitative information into intuitive verbal descriptors (e.g. “long” or “uneven”). A case study in the city of Augsburg exemplarily shows how a fuzzy inference system helps to make a selection from three alternative bike routes. These are based on three exemplary measures ( length, elevation and pavement roughness length ). The results document a defuzzyfied measure helping to select the most suitable route. This approach intends to reduce cognitive load, supports informed selection of routes, and aligns more closely with how users perceive real-world biking conditions. Our work contributes a methodological foundation for integrating fuzzy logic into route selection. We thereby intend to offer an alternative to conventional route selection strategies dominated by rigid cost-based models.