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Driving behavior is considered a unique driving habit of each driver and hasa significant impact on road safety. Classifying driving behavior andintroducing policies based on the results can reduce the severity of crashes onthe road. Roundabouts are particularly interesting because of theinterconnected interaction between different road users at the area ofroundabouts, which different driving behavior is hypothesized. This studyinvestigates driving behavior at roundabouts in a mixed traffic environmentusing a data-driven unsupervised machine learning to classify driving behaviorat three roundabouts in Germany. We used a dataset of vehicle kinematics to agroup of different vehicles and vulnerable road users (VRUs) at roundabouts andclassified them into three categories (i.e., conservative, normal, andaggressive). Results showed that most of the drivers proceeding through aroundabout can be mostly classified into two driving styles: conservative andnormal because traffic speeds in roundabouts are relatively lower than in othersignalized and unsignalized intersections. Results also showed that about 77%of drivers who interacted with pedestrians or cyclists were classified asconservative drivers compared to about 42% of conservative drivers that did notinteract or about 51% from all drivers. It seems that drivers tend to behaveabnormally as they interact with VRUs at roundabouts, which increases the riskof crashes when an intersection is multimodal. Results of this study could behelpful in improving the safety of roads by allowing policymakers to determinethe effective and suitable safety countermeasures. Results will also bebeneficial for the Advanced Driver Assistance System (ADAS) as the technologyis being deployed in a mixed traffic environment.

How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning