## 6 Discussion  
Most previous road safety research has focused on established motorways. Accident data, real-world vehicle testing, and naturalistic driving are typically used in these studies. However, driving simulation technology offers opportunities to improve the safety of newly built roads and complex motorways. Continuous vehicle operating data, driver control data, etc., are collected in a controlled experimental environment [37]. Moreover, mountainous freeways pose additional challenges for autonomous driving and driver assistance systems due to the complexity of horizontal and vertical curves. Safety designs for combined curves need be approached from multiple dimensions. Recent research has highlighted the importance of analyzing combined curves [38]. Prior studies have developed statistical models to investigate the correlation between the geometric design of highway and vehicle operation such as speed differential, lateral acceleration, and vehicle trajectory [39; 40]. To better understand the driver's performance when driving in a combined curve, this study converts the speed difference into the speed change behavior and converts the vehicle trajectory data into the lane departure behavior. It can reflect the driver's adaptability to the road alignment intuitively. From the data distribution characteristics of the micro-driving behavior on the four types of combined curves, we can also intuitively  
Figure 17: The SHAP values for the geometric design parameters of ADCF lane departure behavior on crest curve. see that the micro-driving behavior of the four types of combined curves is different. By comparing the coefficient of variation, we confirm this point that for different combined curves, different driving behavior should be paid more attention. It can also provide new ideas for the safety evaluation of combined curves. In the case of sag curves, the coefficient of variation of the driver's speed difference is larger than the lane departure, and the speed is discrete, indicating that the speed change behavior of sag curves needs more attention. Some scholars have used stratified negative binomial analysis to analyze the collision frequency of sag curves, and the results also indicate that speed, horizontal curvature, and slope all have a significant impact on the collision frequency of sag curves [38]. The curve speed warning, adaptive cruise control, etc., and the speed-related driver assistance systems need to be concerned with these two types of combined curves. For downslope curves, upslope curves and crest curves, the lane departure warning, lane centering control, and lane departure prevention, etc., should be concerned with these three types of combined curves. The coefficient of variation of the driver's speed difference in downslope driving is larger than in other types of combined curves. The order of importance of geometric design parameters generally remained consistent between the RF and RF-SHAP models, but there were instances where the order of importance of individual variables varied significantly. For example, there were notable discrepancies in the IDCF departure models for upslope curves (\(G_{mean}\)), crest curves (\(L_{cc}\), R), and the ADCF departure model for crest curves (\(\Delta G\)). In addition, there were cases where the design parameter importance rankings differed significantly between the RF and RF-SHAP results, such as the speed variation models for downslope and sag curves. The discrepancies in design parameter importance rankings between RF and RF-SHAP models primarily stem from differences in how they calculate feature importance. The RF model typically employs impurity-based metrics like Gini impurity or information gain to assess feature importance, based on the effectiveness of feature splits at decision tree nodes, thereby measuring the contribution of features to the model's predictive accuracy.