On the use of clustering analysis for identification of unsafe places in an urban traffic network

Johan Holmgren*, Luk Knapen, Viktor Olsson, Alexander Persson Masud

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


As an alternative to the car, the bicycle is considered important for obtaining more sustainable urban transport. The bicycle has many positive effects; however, bicyclists are more vulnerable than users of other transport modes, 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 used an iterative k-means clustering approach, which is a contribution of the current paper, DBSCAN. In contrast to standard k-means clustering, our iterative k-means clustering approach enables to remove outliers 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.

Original languageEnglish
Pages (from-to)187-194
Number of pages8
JournalProcedia Computer Science
Early online date14 Apr 2020
Publication statusPublished - 2020
Event11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops - Warsaw, Poland
Duration: 6 Apr 20209 Apr 2020


  • bicycle impediment
  • Click-point data
  • Cluster analysis
  • iterative k-means
  • k-means


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