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<p>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&#xa0;fuzzy inference system to enhance route selection. The idea is to model subjective and qualitative characteristics with fuzzy logic. By comparing routes from a&#xa0;set of alternatives for a&#xa0;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&#xa0;case study in the city of Augsburg exemplarily shows how a&#xa0;fuzzy inference system helps to make a&#xa0;selection from three alternative bike routes. These are based on three exemplary measures (<i>length, elevation </i>and <i>pavement roughness length</i>). The results document a&#xa0;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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Pablo S. Löw,
  • Jukka M. Krisp,
  • Andreas Keler

摘要

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.