An iterative k-means clustering approach for identification of bicycle impediments in an urban traffic network

Abstract

The bicycle has many positive effects; however, bicyclists are more vulnerable than users of other transport modes, and the number of bicycle related injuries and fatalities are too high. We present a clustering analysis aiming to support the identification of the locations of bicyclists' perceived unsafety in an urban traffic network, so-called bicycle impediments. In particular, we present an 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 in the city of Lund, Sweden, where each data point defines a location and time of a bicyclist's perceived unsafety. The results of our study show that 1) clustering is a useful approach in order to support the identification of perceived unsafe locations for bicyclists in an urban traffic network and 2) it might be beneficial to combine different types of clustering to support the identification process. Furthermore, using the adjusted Rand index, our results indicate high robustness of our iterative k-means clustering approach.