Analyzing Factors that Influence Expressway Traffic Crashes Based on Association Rules: Using the Shaoyang–Xinhuang Section of the Shanghai–Kunming Expressway as an Example
Bibliographic record
Abstract
Analyzing the influencing factors in expressway traffic crashes is an important aspect of traffic safety analysis. This study examines detailed data from 217 crashes that took place from 2015 to 2016 on the Shaoyang–Xinhuang section of the Shanghai–Kunming Expressway. The data were recorded by the Expressway Administration of Hunan Province, China, which is the only officially available and reliable source of traffic accident data. Descriptive statistics for crash characteristics, vehicle conditions, and road environmental condition data are provided. This paper studies the characteristics of expressway traffic accidents and their influencing factors via the association rule data mining method. The 21 rules obtained from the association rules were analyzed, the accident characteristics of various types of vehicles were studied, and the causes of injury/fatal accidents in various situations were identified. The results of this study will contribute to the targeted enforcement of traffic regulations and the improvement of road facilities to reduce traffic casualties and promote road safety in other areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".