###### Abstract  
The correct identification and timely pre-warning of driving behavior risks can remind drivers to correct their unsafe driving behaviors effectively. First of all, four risk evaluation indicators of driving behavior were defined based on lateral and longitudinal driving characteristics: the lateral stability indicator, the longitudinal stability indicator, the car-following risk indicator, and the lane-changing risk indicator. The Pearson correlation coefficient method was used to analyze the correlation of the four indicators, and the conclusion showed that the four indicators were very weakly correlated or presented an irrelevant correlation. Thus, the four indicators can describe different driving behavior risks. Secondly, the criteria importance through intercriteria correlation (CRITIC) method was used to determine the weight of each indicator, and a comprehensive measurement model of driving behavior risk was established. To test the model, this study preprocessed the trajectory data of small vehicles in Lanes 1-5 of the I-80 Expressway from the NGSIM dataset, collected statistical analysis results of vehicle speed and acceleration, and obtained the parameters data required for risk assessment. Then, based on the obtained trajectory data, the variation laws and the thresholds of the four indicators were determined by using the interquartile difference method. Finally, by using the K-means clustering algorithm, the risk types of driving behavior were divided into four categories, namely, dangerous, aggressive, safe, and conservative. The dangerous, aggressive, safe, and conservative driving behaviors accounted for 5.40%, 23.30%, 43.22%, and 28.08% of the total samples, respectively. The expert's  
[PERSON] et al. [5] established an abnormal driving behavior management system and adopted mitigation strategies to improve the safety of all test vehicles by about 10 times based on the actual traffic data in Virginia. As a measure of a driver's various operational performances during driving, driving behavior is affected by many aspects. [PERSON] et al. [6] reviewed natural driving research and data collecting methods, discussed various factors that influence the driving behavior, and found that the driving behavior was the main cause of most road accidents. [PERSON] et al. [7] used the SHRP2 large-scale questionnaire to study the relationship between demographic characteristics, sensation seeking, risk perception, and dangerous driving behavior, finding that lower levels of sensation seeking and higher levels of risk perception can serve to inhibit dangerous driving behaviors. [PERSON] et al. [8] studied the relation of personality variables and driving behavior safety and concluded that the driver's personality with a high sense of seeking and a low sense of responsibility is closely related to a higher mean speed and a higher risk behavior during driving. Currently, there are three methods for studying the driving behavior: questionnaire surveys, driving simulations, and algorithmic models. Among them, questionnaire surveys require a large amount of manpower, and the cost of driving simulations is high. It is most suitable to use an algorithm model for the evaluation of the driving behavior. [PERSON] et al. [9] used the centrality function C-Measurement to classify drivers' behavior; they proposed a formula to quantify the driving behavior and divided it into four categories by combining the graph theory and social psychology. [PERSON] et al. [10] proposed a new pattern recognition algorithm for unsupervised driving behavior data to discover the common driving behavior patterns for each cluster. [PERSON] et al. [11] collected data for different driving behaviors through a driving simulator, including maximum speed, lateral position, and distance from the preceding car, and compared the objective data retrieved by the driving simulator with the resulting scores of a questionnaire. The results showed that people may have a different understanding of the safety level of driving behaviors.