Investigating the severity of non-urban road traffic accidents in typical regions of Sichuan and Guizhou, China
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".