The bicycle has many positive effects; however, bicyclists are more vulnerablethan users of other transport modes, andthe number of bicycle related injuries and fatalities are toohigh.We present a clustering analysis aiming to support the identification of the locations ofbicyclists' perceived unsafety in an urban trafficnetwork, so-called bicycle impediments.In  particular,  we presentan  iterative  k-means  clustering approach,  which  in  contrast  to  standard  k-means  clustering, enables to remove outliers and solitary points from the data set. In our study, we used data collected by bicyclists travelling inthe city of Lund, Sweden, where each data point defines a location andtime of a bicyclist's perceived unsafety.The results of our study show that 1) clustering is a usefulapproach in order to support the identification of perceived unsafelocations forbicyclists in an urban traffic networkand2) it might bebeneficial to combine different types of clustering to support theidentification process. Furthermore, using the adjusted Rand index, our results indicate highrobustness of our iterative k-means clustering approach.