Investigating the severity of non-urban road traffic accidents in typical regions of Sichuan and Guizhou, China

Abstract

Objective

The traffic characteristics of Sichuan and Guizhou differ from those of other regions due to its unique geographical features. In addition, accident studies in China mainly focus on urban roads in the eastern and central regions. However, studies on western regions, especially non-urban roads, are scarce. Thus, this study aims to explore the factors that influence the severity of accidents on non-urban roads in typical regions of Sichuan and Guizhou.

Methods

A total of 541 cases from 2014 to 2020 were selected from the database of the China In-Depth Accident Study, where 18 variables, which may exert an impact on accident severity, were extracted after screening. First, heterogeneity of data was eliminated through latent class analysis (LCA). The ordered probit (OP) model was then conducted for each class to obtain significant variables that exert an impact on accident severity. The study quantified the degree of influence of the significant variables using marginal effect analysis.

Results

The LCA results demonstrate that data were categorized into the following classes, namely, (a) two-vehicle accidents involving trucks, (b) pedestrian and multiple-vehicle accidents, (c) two-wheeler accidents, and (d) single-vehicle accidents. The OP results show that most variables could exert impact on accident severity, and some of them exerted varying levels of influence on the severity of different classes, whereas others only influence a specific class.

Conclusion

According to this study, we obtained the accident characteristics of these regions and put forward some targeted suggestions to further improve the level of road traffic safety. The findings can provide support for the construction of transportation in line with the regional characteristics in China.

Keywords:

• Non-urban roads
• latent class analysis
• ordered probit
• accident severity

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No.52172399, No. 51875049), the Key Research and Development Program of Hunan Province (No. 2020SK2099), the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 21A0193, No. 21B0324). This work was supported in part by National Key R&D Program of China (2017YFE0118400, 2019YFE0108000).