Rethinking Bikeability Indexes: Fusing Knowledge Graph and MCDA Technique for Multi-criteria Bike Network Evaluations

Abstract. The evaluation of bike networks is important for improving cycling infrastructure and supporting sustainable travel behaviors. While bikeability indexes are widely used, current methods typically rely on straightforward metric aggregation, often overlooking nuanced interactions between evaluation elements. In this study, we introduce a novel approach to integrate a Knowledge Graph (KG) of bikeability evaluation studies with the Analytic Network Process (ANP), a decision modeling technique. The KG, which comprises more than 270 bikeability metrics and 41 qualitative criteria, provides a structured foundation for index development, reflecting the trends in existing evaluation approaches. ANP enhances this framework by capturing the interdependencies in the use of qualitative criteria and quantitative metrics, ensuring a more rigorous and transparent aggregation process. As a case study, we apply this methodology to Zurich’s road network and evaluate network bikeability at the segment level. Further, we conduct a sensitivity analysis of how changes in the KG structure impact the network evaluation results. By treating bikeability index development as a decision-making task, our study strengthens the methodological foundation of bikeability index design. Future research will scale this framework to larger networks, extend the sensitivity analysis, and benchmark our approach against established bikeability indexes.