# Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions
## 1 Introduction
By establishing models, researchers aim to identify the key factors influencing the severity of heavy truck traffic incidents, thereby guiding traffic management departments in targeted prevention and control. Given the discrete nature of severity data and the need for model interpretability, logistic regression models are frequently employed to analyze accident severity [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In previous studies, scholars have commonly examined multiple dimensions in selecting independent variables, including driver, vehicle, road, environment, and violations, as shown in Table 1.  
By modeling the above independent variables, researchers have drawn the following conclusions: Regarding driver factors, driver age and experience significantly impact acci \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimensions** & **Variables** & **References** \\ \hline \multirow{3}{*}{Driver} & Age & [17; 18; 19; 20; 22] \\  & Gender & [17; 18; 20; 22] \\  & Driving experience & [17; 19; 24; 25] \\ \hline \multirow{3}{*}{Vehicle} & Accident forms & [16; 18; 25] \\  & Accident types & [20] \\ \hline \multirow{3}{*}{Road} & Road alignment & [18; 20; 21; 24] \\  & Road surface conditions & [18; 22; 23] \\ \hline \multirow{3}{*}{Environment} & Weather & [17; 18; 20; 22; 23; 24; 25] \\  & Lighting conditions & [17; 19; 21; 22; 24] \\  & Visibility & [17; 23] \\ \hline \multirow{3}{*}{Violations} & Speeding & [18; 21; 24; 25] \\  & Drunk Driving & [21; 24; 25] \\ \cline{1-1}  & Distracted Driving & [18; 24; 25] \\ \cline{1-1}  & Unqualified Driver & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of the independent variables. dent severity. Younger, less experienced drivers are more likely to be involved in severe accidents [18; 19; 23; 24]. Regarding road factors, road alignment significantly affects accident severity, with curved and sloped sections increasing accident severity [18; 20; 21]. Regarding environmental factors, weather and lighting conditions significantly influence accident severity, with adverse weather (rain and snow), low-light areas, and nighttime conditions more likely to result in severe injuries [18; 19; 20; 21; 22; 23]. Regarding violation factors, driver misconduct significantly increases accident severity, with distracted driving, speeding, and drunk driving notably escalating accident severity [18; 21; 24; 25]. Additionally, researchers conducted comparisons targeting specific variables, resulting in the following findings: [PERSON] et al. [20] compared the severity of single-vehicle (SV) and multi-vehicle (MV) accidents involving heavy trucks and discovered significant differences in how various factors--such as driver, vehicle, road, and environmental variables--affect accident severity. [PERSON] et al. [21] compared the causes of heavy truck accidents under normal and adverse weather conditions, finding that the factors contributing to accident severity vary considerably between the two weather scenarios. [PERSON] et al. [22] compared the causes of heavy truck accidents in rural and urban areas, revealing that the causes differ significantly depending on the regional context and recommended separate modeling studies for different types of areas. In summary, while current research has examined the causes of heavy truck accident severity from multiple dimensions--including driver, road, and environmental factors--most of this research is based on datasets from the United States, with some studies using data from Canada, South Korea, and other countries. There is a notable lack of modeling analyses of heavy truck accident severity based on Chinese datasets. Due to substantial differences in laws, regulations, traffic management measures, and road standards between China and other countries, research conducted on foreign datasets may not be entirely applicable to China. Furthermore, previous studies have shown that factors impacting heavy truck accident severity differ considerably across contexts, underscoring the need for targeted, context-specific research.