# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 6 Results
### SHAP Analysis
Figure 12d shows an interaction effect between the PDW and age. When the proportion of driving time during weekdays was less than 0.87, there was no significant change in the accident risk of bus drivers. However, when the proportion exceeded 0.87, different age groups of drivers showed a significant difference in accident risk. With a higher number of driving hours during the week, the accident risk for older bus drivers decreased, while the accident risk for younger bus drivers remained high. As the PDW got higher (closer to 1), the SHAP of the red point was less than 0. Older drivers reduced the SHAP value of the PDW. In other words, increasing age minimized the effect of PDW on accidents. In addition to the previous analysis, this study further compared the risk profiles of bus drivers by plotting the feature impact heat maps for samples with the label 1 (Figure 13a) and label 0 (Figure 13b). The \(x\)-axis represents the sample sequence with data points below. The color of each data point stands for the magnitude of its feature value, signifying the degree of influence on the accident risk. The red represents a higher feature value, showing a positive impact relationship. Taking \"Rainfall\" as an example, approximately 0-180 samples in Figure 13a and 0-20 samples in Figure 13b are shown in red, indicating that these bus drivers were strongly affected by higher rainfall. At the same time, it can be visualized that Figure 13a is dominated by red and Figure 13b is primarily blue, demonstrating that features for drivers in group (a) have a positive correlation with their risk level, while features for drivers in group (b) have a negative correlation. Figure 12: Partial characteristic influence interaction diagrams. Furthermore, the \(y\)-axis is sorted based on the importance of the features. It is noted that there are slight differences in the impact features between the different categories of drivers. In Figure 12(a), the variables \"Rainfall\" and \"SDEE\" have a more prominent impact compared to Figure 12(b). This difference points out that compared to bus drivers with a lower accident risk, \"Rainfall\" and \"SDEE\" play a more significant role in predicting the accident risk for bus drivers with a higher risk. The variation in the impacts of different features between the two driver categories provides valuable insights into the factors contributing to higher accident risk in certain drivers. By understanding these differences, appropriate measures and interventions can be developed to improve road safety and reduce the occurrence of bus accidents. Figure 13: Maps of different tag set features. The SHAP interpretability method not only visualizes the accident propensity of a population of bus drivers, but for a single sample (i.e., a single bus driver), it also shows the mechanism by which their characteristics influence the occurrence of accidents. Figure 14 is a waterfall diagram used to locally interpret a positive and negative sample (selected using simple random sampling). Each arrow indicates the direction and magnitude of the feature's effect on the target. The graph starts at the bottom with the expected value \(E(f(X))\) predicted by the model and progressively shows the contribution of each feature to the expected value. Based on the principle of linear additivity, the results of each feature's contribution were added together to provide the final prediction. As shown in Figure 14, the primary factors and their levels of importance are distinct for different drivers. In Figure 14, the indicators that had a more significant impact on  
Figure 14: Waterfall diagrams with different labels. accident probability for the bus driver were rainfall, 0.99, age, 33, driving experience, 1, ADW, 31.5, etc., and most features had a positive effect. With these features' positive and negative effects, the model predicted a result of 1 for this driver, implying that an accident would occur.