# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 6 Results
### SHAP Analysis  
Prior research has identified correlations between accident occurrence and various factors, including but not limited to individual bus driver characteristics, work hour characteristics, violation and risky behavior characteristics, and so on. Despite this prior knowledge, the exact influence of these characteristics on the probability of accidents still needs to be determined. At the same time, the CatBoost model constructed in the previous section is a black-box model, which does not clearly show the mechanism of the influence of each characteristic on the probability of accidents. Therefore, the SHAP interpretable method was adopted in this paper to visualize and interpret the CatBoost results. Figure 9 displays the average contributions of every feature. The horizontal axis is each feature's average absolute SHAP values, representing their impact on the prediction variable. The vertical axis is arranged according to the importance of the variables, with higher positions indicating more significant influence. In Figure 9, the features \"Age\" and \"Rainfall\" are ranked at the top, with average SHAP values of 0.11 and 0.1, respectively, significantly surpassing the other features. This suggests that age and rainfall are strongly related to the accident risk of bus drivers. The next are \"Driving Experience\" and \"Azimuthal Change\". Following that are driving characteristics (e.g., \"the proportion of driving duration during peak duration\" (PDP), \"the accumulated driving duration of the previous week\" (ADW), and \"the proportion of driving duration within the week\" (PDW)), operational characteristics (e.g., \"speed dispersion when entering and exiting stations\" (SDEE) and \"Average Speed\"), as well as the \"time since last violation\" (TLV) and \"Education\". Figure 8: Comparison of PR curves for different interpolated datasets. Figure 10 shows the relationships between the features and the predicted target variables. It covers the SHAP values of each feature for each sample. The colors represent the magnitude of the feature value (red indicates a higher feature value and blue indicates a lower feature value) and the horizontal axis indicates the importance of the influence on the target variable, shown as a SHAP value. For example, the red dots for age and driving age are mainly located on the left side of the horizontal axis, which indicates that when the eigenvalues of the age and driving age variables of the bus drivers were large, the impact on the model was relatively tiny (the SHAP values are negative). This means that the older the bus driver's age and driving age are, the less likely that accidents will occur. This impact on the model was relatively small, which means that the older the bus driver is, the lower the probability of an accident. Similarly, the red dots, according to the length of the last violation, azimuth change, etc., are mainly located on the right side of the horizontal axis, indicating that the longer the bus driver has gone since the last violation and the greater the degree of change in their azimuth angle, the greater the possibility that the bus driver will be involved in an accident. Figure 9: Feature importance ranking chart. To further explore the relationship between bus driver accident risk and each feature, SHAP dependency plots were plotted for the top 12 ranked features, as shown in Figure 11. Each point in the plot is a sample. The histogram shows the distribution of each feature, while the trends visualize the impact of the variables on the model output. By analyzing the trends and patterns of the sample points in the plots, we can better understand how each feature contributes to the overall risk. Figure 10: SHAP summary plot. Regarding the features \"Age\" and \"Driving Experience\", as the age and driving experience of bus drivers increase, their SHAP values gradually decrease from positive to negative. This revealed that younger and less experienced bus drivers tend to have a higher accident risk.