Abstract
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 language | English |
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Pages (from-to) | 187-194 |
Number of pages | 8 |
Journal | Procedia Computer Science |
Volume | 170 |
Early online date | 14 Apr 2020 |
DOIs | |
Publication status | Published - 2020 |
Event | 11th 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 2020 → 9 Apr 2020 |
Funding
The presented study was conducted as part of the project Smart public environments II, which was funded by Sweden’s Innovation Agency (Vinnova). We acknowledge Trivector for providing the data used in the study.
Keywords
- bicycle impediment
- Click-point data
- Cluster analysis
- DBSCAN
- iterative k-means
- k-means