## 6 Discussion
In contrast, the RF-SHAP model integrates the SHAP method, which considers the impact of each feature on each sample and the contribution of different feature values to the model output. Feature importance in the RF-SHAP model is derived from statistical computations of SHAP values for each feature, providing the average contribution of each feature to the model output. Unlike the RF model, the SHAP values in the RF-SHAP model not only indicate the contribution of each feature to the overall model output but also illustrate the influence of each feature value on the model output for each sample. This allows for a more detailed analysis of the impact of individual feature values on model predictions, leading to a comprehensive understanding of model behavior. Additionally, RF-SHAP yields more robust results by considering the contribution of each sample's features rather than relying solely on aggregate dataset statistics, making it more resilient to data with imbalanced distributions or outliers. Consequently, the feature importance results from the RF-SHAP model were deemed more reliable for subsequent analysis in the study. From the results of the RF model and the SHAP values, we can see that the geometric design parameters that affect micro-driving behavior are different for different combinations of line shapes. For example, for sag curves, the top four geometric design parameters affecting speed change behavior are the length of the departure transition, the length of the circular curve, the radius, and the slope change of the following section. For the upslope curve, the top three geometric design parameters affecting the ADCF lane departure are \(\Delta G\), \(L_{cc}\),\(R\). For the crest curve, the top three geometric design parameters affecting the IDCF and ADCF lane departure are \(L_{cc}\), \(\Delta G\), and \(L_{dt}\). This is consistent with existing research, as studies have indicated that the length of overlap between horizontal curves and sag curves (\(L_{cc}\)) has a significant impact on lateral acceleration [17]. Lateral acceleration also directly influences lane departure values. From another perspective in this study, when improving the design of the combined curves, we can gather which design parameters should be prioritized and which design parameter will be the focus for optimization. In fact, the process of optimizing the design of combined curves could be summarized based on the research:  
* Selecting key safety evaluation measures. Speed change and lane departure behavior are selected as the safety evaluation measures of combined curves. * Ranking of geometric design parameters of combined curves. The quantitative relationships between geometric design parameters of combined curves and safety evaluation measures should be established. Significant geometric design parameters could be obtained. The priority of each design parameters could be determined using the SHAP value. * Optimizing design based on safety evaluation measures. Priority must be given to the geometric design parameters with the largest SHAP value for design optimization. Previous studies have demonstrated that drivers have different micro-driving behaviors when driving on different types of combination curves, while identifying geometric design parameters that have a significant effect on micro-driving behavior [12]. In this study, the RF-SHAP model was used to quantify the importance of roadway geometric design parameters on speed change and lane departure on different combination curves. However, due to the limitations of the sample data, which was not combined with accident data and natural driving data, the causal relationship between the independent and dependent variables needs to be better validated.