###### Abstract
The measurement of driving behavior risks is also a popular research field. However, the majority of data utilized in driving behavior risk measurement is derived from videos and simulation experiments. The length of road sections captured by video is limited, and simulation experiments struggle to provide accurate driving behavior trajectory data. In recent years, with the acquisition of a vast quantity of natural driving trajectory data, it has become possible to conduct in-depth research on vehicle driving behavior. [PERSON] et al. [12] proposed a new improved traffic conflict indicator, based on trajectory data collected using unmanned aerial vehicles (UAVs), that builds upon the strengths of conventional indicators and addresses their limitations. These indicators require the definitions and calculations for three types of traffic conflicts (near-end, lane change, and with fixed objects) that accurately reflect real traffic risks. [PERSON] et al. [13] designed accurate algorithms to extract vehicle trajectory data from video data collected by UAVs and then proposed a lane-changing risk factor. This risk factor can provide a safety evaluation for the target vehicle and adjacent vehicles during lane change. [PERSON] et al. [14] proposed a real-time driving behavior safety level classification and evaluation framework based on driving behavior data and divided the driving behavior into normal driving, low-risk driving, and high-risk driving by using the k-means clustering method, the hierarchical clustering method, and the model-based clustering method, respectively. Based on the analysis of a large number of trajectory data, [PERSON] et al. [15] proposed an abnormal driving pattern detection method based on outlier detection to detect the driving behavior of drivers. This method does not require specific data for normal driving behaviors and various abnormal driving behaviors and is very reliable in detecting abnormal vehicle-driving behaviors. In terms of risk measurement for driving behavior, the extant research primarily concentrates on evaluating traffic conflicts and predicting the likelihood of traffic collisions, while lacking a comprehensive risk assessment of various driving operations performed by drivers. Concurrently, the existing research on driver classification focuses on considering a driver's overall driving style and lacks an evaluation of the driver's driving behavior risk at each moment. Furthermore, research on driving behavior could be applied in many frontier research fields, such as autonomous driving and ecological driving, in the future. [PERSON] [16] used the autonomous driving rule manual to analyze the safety of driving behavior, formulated the driving behavior of driverless vehicles through the precise definition of rules, and made good use of trajectory violation indicators to help identify specific unsafe scenes. [PERSON] et al. [17] proposed a hybrid model composed of the cloud model and the Elman neural network to predict dangerous driving behaviors based on vehicle motion state estimation and passenger subjective evaluation, which could provide a practical solution for safe driving. Based on research into the rules governing the association between driving behavior and accident risk, the individual driving behavior can be quantitatively evaluated and utilized as an index for assessing the accident risk. The research findings related to the driving behavior can be applied to driver training, enhancing driving safety, developing driver behavior recommendation systems, or formulating personalized insurance for vehicles, and hold significant value. In summary, the extant research on driving behavior risk measurement is not comprehensive and lacks a well-suited model for the real-time evaluation of driving behavior risks.