# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 8 Conclusions  
This research developed a model for predicting and analyzing the probability of accidents among bus drivers, taking into account their occupational characteristics. The main conclusions are the following:  
1. The CatBoost method was used for bus driver accident probability prediction. The missing data were filled in based on the MICEForest multiple interpolation method. The CatBoost model was adjusted by the Bayesian hyperparameter optimization method with AUC-PR as the optimization objective. After optimization, the prediction model reached an AUC of 0.834, an AUC-PR of 0.788, and a Cohen's kappa (k) of 0.68 for the test set. They were 1.83%, 1.68%, and 6.79% higher than LightGBM, respectively, 5.84%, 6.92%, and 18.06% higher than XGBoost, respectively, and 7.89%, 9.00%, and 9.00% higher than GBDT, respectively. Meanwhile, the MICEForest interpolated dataset had a significant advantage over the single mean computation method, as evidenced by the ROC and PR curves.
2. The predictive model was explained by the SHAP interpretable method. The most important features were age and daily rainfall. Next, came driving experience, azimuthal change, etc. In addition, this study innovatively found that the peak driving duration, the degree of dispersion of entry and exit speeds, the duration of the last violation, and the weekday driving duration of a bus driver are related to their accident risk. These results were also discussed and visualized with the help of SHAP, regarding group and individual accident probability mechanisms, interaction effects between the features, and accidental and non-accidental group accident tendencies.  
In conclusion, this study provides a more scientific method and valuable insights for predicting the probability of bus driver accidents. It is significant for related companies to identify the potential risks of bus drivers, scientifically adjust drivers' work schedules, and promptly formulate cyclical education and training strategies. However, there are some limitations; for example, only one month of bus driver accident data in one region were selected, resulting in a single environment. This is because the state-funded project this paper relied on is still in progress, and more data are yet to be available. But, as the project demonstration progresses, we will collect more cities and a more extended sample dataset. This expansion will help in assessing the model's predictive power and adaptability more accurately. At the same time, we will consider adding more featuresthat affect the probability of bus driver accidents to provide more theoretical support and practical guidance for accident risk prevention and control.  
Conceptualization, T.D., L.Y., J.X. and K.Z.; methodology, T.D. and L.Y.; formal analysis, T.D. and K.Z.; writing--original draft, L.Y. and K.Z.; writing--review and editing, T.D., L.Y. and J.X.; supervision, J.X.; data curation, Z.L.; project administration, T.D., Z.L. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2021 YFC3001500) and Scientific and the Jilin Province Transportation Innovation Development Support Project titled 'Dynamic Evaluation Technology Research for Rural Road Development in Jilin Province'.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.