Micro-Driving Behavior Analysis of Drivers in Congested and Conflict Environments Using Graph Theory

Many traffic conflicts on the roads are caused by a small proportion of drivers. Currently, there are few studies exploring the time-varying patterns of driving behavior among these drivers. This paper proposes a generic time-series analytical framework and uses it to analyze the driving behavior patterns of many high-risk drivers, which provides a theoretical and targeted basis for vehicle warning systems. Specifically, the natural trajectory time-series data in the rear-end conflict process from congested highway sections were first obtained. Secondly, K-medoid clustering was utilized to obtain the quantitative driving behavior sequence from the trajectory. Thirdly, the driving behavior sequence was transformed into a graph structure by the co-occurrence matrix. Graph theory and Markov theory were used to analyze the obtained graph to achieve the goal of analyzing the time-varying patterns of driving behavior. The analysis found that the driving behavior transition graph network of high-risk drivers on congested highway sections does not exhibit the small-world property and this suggests that during the conflict process, the driver is unable to quickly transition between states. Additionally, vehicles consistently evolve into a rear-end conflict state along a fixed driving behavior transition route, which indicates that the causes of conflicts in congested road sections are similar. Finally, the state change of the conflict process follows the Markov property, proving that the state during the conflict process can be predicted and controlled.