  Driving behavior is considered a unique driving habit of each driver and has
a significant impact on road safety. Classifying driving behavior and
introducing policies based on the results can reduce the severity of crashes on
the road. Roundabouts are particularly interesting because of the
interconnected interaction between different road users at the area of
roundabouts, which different driving behavior is hypothesized. This study
investigates driving behavior at roundabouts in a mixed traffic environment
using a data-driven unsupervised machine learning to classify driving behavior
at three roundabouts in Germany. We used a dataset of vehicle kinematics to a
group of different vehicles and vulnerable road users (VRUs) at roundabouts and
classified them into three categories (i.e., conservative, normal, and
aggressive). Results showed that most of the drivers proceeding through a
roundabout can be mostly classified into two driving styles: conservative and
normal because traffic speeds in roundabouts are relatively lower than in other
signalized and unsignalized intersections. Results also showed that about 77%
of drivers who interacted with pedestrians or cyclists were classified as
conservative drivers compared to about 42% of conservative drivers that did not
interact or about 51% from all drivers. It seems that drivers tend to behave
abnormally as they interact with VRUs at roundabouts, which increases the risk
of crashes when an intersection is multimodal. Results of this study could be
helpful in improving the safety of roads by allowing policymakers to determine
the effective and suitable safety countermeasures. Results will also be
beneficial for the Advanced Driver Assistance System (ADAS) as the technology
is being deployed in a mixed traffic environment.
