Analysis of influencing factors for rear-end collision on the freeway
Bibliographic record
Abstract
Rear-end collision accounts for the main type of traffic accidents occurring on the freeway. In order to extract the significant influence factors of rear-end collision on the freeway, this study utilized the data of freeway traffic accidents between 2010 and 2015 in China. First, based on quasi-induced exposure theory, the information of driver, vehicle, and road environment was analyzed. Gender, age, driving age, vehicle safety, load, weather, fatigue, driving speed, road alignment, accident time, and visibility were selected as the important factors that might affect rear-end collision. Second, based on logistic regression model, the influencing factors analysis model of freeway rear-end collision was established. In the regression analysis, the possible important factors selected were taken as the independent variables, and the accident responsibility was taken as the dependent variable. Then, the factors that had significant influence on rear-end collision were selected from candidate independent variables by stepwise regression method. Finally, the specific influence of driving age, load, weather, accident time, visibility, fatigue, and driving speed on rear-end collision occurring on the freeway was discussed. The analysis results were explained according to the odds ratio. The research results of this article can provide guidance for the prevention of rear-end collision on the freeway and theoretical support for the development of freeway early warning system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".