Exploring Fuzzy Systems Descriptive Potential for Bikeability Routing

This paper proposes a methodology for bicycle route classification using fuzzy logic to facilitate intuitive assessments. As cycling is an essential part of transforming the mobility sector toward sustainability, understanding what makes routes attractive to cyclists is critical.Traditional routing systems often rely on fixed and abstract measures, but this study explores whether a fuzzy inference system (FIS) can provide more qualitative and intuitive route descriptions. The approach involves calculating multiple routes through a case-study area in Augsburg, Germany, based on distinct weights: length, elevation, and pavement roughness. These routes are compared to those generated by BRouter, a reference routing service.Results demonstrate that a fuzzy-based method can yield comparable routes and improve route descriptions by offering linguistic attributes. While data inconsistencies in OpenStreetMap (OSM) present challenges, the fuzzy approach provides flexibility in route interpretation. The study highlights the potential for qualitative route descriptions to complement traditional quantitative measures in bicycle navigation systems.