# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 2 Literature Review
However, whether this effect leads to public transportation accidents has been neglected in the current research on accident probability prediction. By the same token, traffic congestion in China differs significantly between weekdays and non-weekdays [22]. Whether this change in a road environment has an impact on the driving performance of bus drivers and thus has some relevance to bus accidents is also unclear. Further, bus drivers also perform the function of transporting and servicing passengers, which is a process that also includes a number of features that affect the risk of accidents. Whether drivers with different driving styles and proficiencies have some variability in their response to the complex driving process of entering and exiting bus stops has been studied [23]. However, the question of whether such differences affect the occurrence of accidents has also not been addressed in current research. Instead, these are part of the occupational characteristics of bus drivers, and this component affects the driving status of bus drivers to varying degrees, thus affecting the occurrence of bus accidents. Incorporating this part of the factors into the process of predicting the probability of bus driver accidents has particular value and practical significance for enriching the feature set of accident prediction. Exploring the occupational characteristics of bus drivers related to accidents can guide management toward targeted management. Predicting the probability of bus driver accidents is actually a typical supervised learning problem. Supervised learning is an important training method in machine learning, which is the process of tuning the parameters of a classifier to achieve the required performance using a set of samples of a known class [24; 25]. Most scholars analyzing the prediction process tend to compare the performance of different popular machine learning algorithms on test sets, in order to find the optimal prediction model suitable for the respective dataset and the actual problem [26; 27]. These prediction methods mainly include linear regression [28; 29], support vector machines [30], various neural networks [27], various types of integrated tree modeling algorithms [31; 32], improved hybrid models [2], and so on. Among them, in the research on traffic accidents, the research on all kinds of tree structures is the most common. Most scholars have found that the integrated model based on the Boosting Trees series has good results in this field through the comparison of all kinds of machine learning algorithms. For example, [PERSON] [33] constructed three highway collision prediction models based on binomial logit, eXtreme gradient boosting (XGBoost), and support vector machine algorithms, respectively, and proved the better prediction performance of the XGBoost model. [PERSON] [34] compared random forests (RFs), AdaBoost with decision trees, gradient-boosted decision trees (GBDTs) and eXtreme gradient-boosted decision tree (XGBoost) models and found that GBDTs were the best choice for predicting the future driving risk of drivers involved in a collision in Kunshan, China. [PERSON] [35] used four boosting families of machine learning algorithms to predict traffic accidents based on three years of traffic accident data on national highways in South Korea, and the results showed that the LightGBM model performed optimally. Thus, for this paper, we utilized the apparent advantages of boosted tree algorithms for the accident probability predictions of bus drivers. Meanwhile, most scholars, in order to explore the main causes of accidents, have used various types of explanatory methods to extract the feature variables that contributed significantly to the prediction model after building the prediction model, in order to overcome the drawbacks of the machine learning black-box model [36]. Among them, SHAP is the most widely used.