Biking as an active way of travelling has many benefits and many cities try to promote it. The first step to informed decisions that increase the bikeability of a city’s street network is to understand where its strengths and weaknesses lie. Therefore one usually creates indices that classify certain aspects of a city’s roads into more positive or negative values. We present a methodology to combine multiple such indices into a fuzzy meta index with fuzzy inference systems in a street network. In the case study area of central Augsburg in Germany we present all indices as histograms to make the way these systems work more visible. There we compare the results of different fuzzy inference systems. The best result is then compared with a standard meta index obtained by the weighted mean of the single indices. The results show that the alternations to the fuzzy inference systems allow adjustments to the fuzzy meta index. We conclude that the methodology presented yields an more adaptive alternative to the weighted average for combining sub-indices.