Analysis of Vehicle Driving Styles at Freeway Merging Areas Using Trajectory Data

Driving style is a comprehensive representation of the driver's internal psychological factors as well as external environmental and traffic management factors. Identifying driving styles can help suggest targeted management strategies and personalized active safety and autonomous driving strategies. This paper analyzes the drivers' driving styles according to the vehicle trajectory data from unmanned aerial vehicle (UAV) videos. Nine characteristic indexes from two aspects of drivers themselves and their interaction with the surrounding vehicles are extracted. The principal component analysis and K-means clustering method are used to reduce the index dimensionality and classify the driving styles of merging vehicles. The results show that the drivers' driving styles could be classified into four categories: aggressive, relatively aggressive, relatively cautious, and cautious. The analysis of the characteristics of different driving styles shows that it is feasible to classify driving styles based on trajectory data.