Driving behavior can be influenced by many factors that are not feasible to collect in driving behavior studies. The research presented in this paper investigates the characteristics of a wide range of driving behaviors linking driving states to the drivers' actions. The proposed methodology is structured such that a known state can be linked to multiple actions, thus accounting for the effects of unknown driving state factors. A two-step algorithm was developed and used for the segmentation and clustering of driving behaviors. The algorithm segments and clusters car-following behaviors based on eight state-action variables: the longitudinal acceleration, the lateral acceleration, the yaw rate, the vehicle speed, the lane offset, the yaw angle, the range, and the range rate. The results of this methodology are state-action clusters that define the driving pattern of drivers. The sample used in this paper included 20 different drivers, i.e., ten car and ten truck drivers. The results revealed that behavior patterns were different between car drivers but not between truck drivers. The results also show that car drivers exhibit behaviors that are unique to each driver, whereas truck drivers show a common driving pattern. The characteristics and frequency of recognized driving patterns are provided in this paper, along with the corresponding modeling parameters of each pattern.Index Terms-Clustering methods, data mining, driver behavior, traffic flow theory.

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