## 2 Materials and Methods
### Variable Selection and Assignment  
The causes of road traffic accidents are mainly from four aspects. Namely, people, vehicles, roads and environment, and many influencing factors are involved in each level. To ensure the rationality of the evaluation indicator, we determined the research variables by the latent Dirichlet assignment (LDA) theme model and expert interviews in the research. As a text mining method, the LDA theme model is a three-layer Bayesian machine learning algorithm that can extract topics automatically from large-scale texts without manual processing. It is a corpus-based statistical model to generate potential topics by clustering. It is usually used unsupervised, facilitating researchers to extract potential topics objectively from massive texts. Specifically, LDA is used in this research to identify the topical semantics of 100 highly cited papers from 2000 to 2022. High-frequency words were selected for factor analysis, and Genism and NumPy libraries were used to construct the LDA model [32] after integrating a specialized thesaurus and deactivating undesirable terms. In this research, the keywords \"road traffic accident\" and \"road traffic safety\" were used to search the _China National Knowledge Infrastructure_ (CNKI) database, and 100 highly cited papers from 2000 to 2022 were collected. After manual screening and eliminating irrelevant topics such as medical and legal literature, 69 papers were retained as the final analysis text. The model perplexity of the analyzed texts is illustrated in Figure 2, where it can be seen that the articles had the least confusion when eight topics were selected. However, the best results were obtained using the seven topics extracted in Table 1. Relevant factors were statistically collated after referring to the subject terms, and interviews were conductedwith relevant experts. A theoretical research model with seven indicators at four levels was constructed in the research (see Figure 3). \begin{table}
\begin{tabular}{c c} \hline \hline
**Topic** & **Word** \\ \hline
0 & prediction, model, neural network, network, method, layer, BP, value, error, degree... \\
1 & road traffic, traffic, impact, death, number of people, vehicle, driver, accident, environment, lighting,... \\
2 & prediction, model, road traffic, accident, impact, death, number of people, traffic, time, highway,... \\
3 & accident, vehicle, traffic, driver, damage, system, car, operation, passenger vehicle, speed,... \\
4 & accident, driving, death, traffic, driver, number of people, vehicle, road traffic, violation, driving experience,... \\
5 & prediction, model, accident, road, desert, number of people, systems, traffic, a section of road, pavement,... \\
6 & accident, death, vehicle, emergency response, driving, traffic, data, personnel, background, management,... \\
7 & accident, driver, motorcycle, death, system, number of people, prediction, fuzzy, method, loss,... \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extraction Results of Road Traffic Accident Themes. Figure 3: Research Model. Figure 2: Topic Puzzlement of LDA Model. According to the theoretical research, we identified the condition variables: DE (driving experience), AG (age of drivers), MM (management model of operating vehicles), BS (business scope of vehicles), RG (technical grade of roads), WE (weather when driving), and LI (lighting condition when driving). The number of variables meets the requirement of the fsQCA method on the number of variables [33; 34]. The dependent variable (outcome variable) is the degree of traffic accidents. When using the fsQCA method, each condition and outcome are considered a set, and each case has an affiliation score in the set [26]. The tool was calibrated for variable data according to the level of affiliation of cases with the characteristic variables, and all data were assigned values between [0, 1], with larger values indicating higher affiliation (apart from control strength).