# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 7 Discussion  
This study focused on a predictive model for bus driver accident risk and analyzed the main influencing factors and mechanisms through interpretability results. By comparing it with three mainstream ensemble learning models, GBDT, XGBoost, and LightGBM, CatBoost was utilized to build a predictive model, and its effectiveness in predicting bus driver accident risk was verified. The well predictive performance of CatBoost was consistent with previous research [54; 55; 56]. With Bayesian hyperparameter optimization, the predictive power and robustness of the CatBoost model were effectively enhanced. It achieved an AUC of 0.834, an AUC-PR of 0.788, and a Cohen's kappa (k) of 0.68 on the test set, improvements of 0.85%, 1.29%, and 3.82%, respectively, over the default parameters. The reliability of Bayesian hyperparameter optimization has also been confirmed in previous studies [49; 57; 58]. The visualization of the model made by SHAP revealed 12 critical indicators with a high impact on the risk. By comparing the findings with previous research, both similarities and differences were identified, allowing for further insights to be gained. In terms of individual characteristics, age and driving experience showed a similar influence trend. As age and driving experience increase, accident risk decreases, which aligns with previous findings [59]. This could be attributed to the fact that older drivers tend to have a more composed mindset and a milder driving style, exhibiting higher levels of safety. Likewise, drivers with higher driving experience are at lower risk probably because of their better driving skills and wealth of experience [60]. But in contrast, the findings regarding bus drivers' education level in this study differ from a previous conclusion [61]. Drivers with a higher level of education (categorized as 3) showed a higher level of risk. One reason for this may be the considerable variation in the number of bus drivers with different levels of education, especially since the more educated drivers (categorized as level 3) made up only 5% of the dataset. Another reason may be that this segment of bus drivers tends to be younger and more susceptible to the higher importance factor \"Age\", as shown in Figure 11(c). However, this result also emphasizes the skill-based nature of the bus driver occupation, which means that a bus driver's driving operational skills and proficiency under different road conditions, as opposed to theoretical knowledge, more profoundly affect the safety of their bus operations. Undoubtedly, the current day's rainfall is the most important factor. This has been proven by [PERSON] [62] and [PERSON] [63]. Our study found that bus drivers' accident risk tended to increase during moderate rain or more. This phenomenon could be attributed to the condition of the road surface. When it is drizzly, there is only a slight change in road surface humidity, while as the rainfall continues to increase, completely wet pavements or even puddles lead to a sharp decrease in the friction coefficient. Vehicles are more prone to sideslip in this environment, triggering traffic accidents. Simultaneously, heavy rainfall affects drivers' visibility and judgment, reducing their perception of surrounding conditions and unexpected situations, which can compromise driving safety. Additionally, bus drivers' accident risks diverge when rainfall is close to zero. Some drivers show relative safety, while the risk of accidents for other public transport drivers increases instead. This disparity implies a lesser impact of rainfall under favorable driving conditions, showing the heterogeneity among individuals. The correlation between the driving duration of bus drivers and their accident risk has been confirmed, consistent with previous studies [5; 39]. Among our studies, the most notable is the proportion of peak driving duration (PDP). The accident risk of bus drivers was higher when their peak driving hours were under three hours (peak driving hours as a percentage of less than 0.3).