Exploring the Causality of Accident Severity on Mountainous Freeways With a Two‐Stage Approach
Bibliographic record
Abstract
Studies on accident severity on mountainous freeways have predominantly centered on the personal injury level, rather than the aggregation level. However, for quantifying the accident causality, clustering the accident severity from multidimensional perspectives based on data‐driven approach is seldom investigated in existing studies. To address this research gap, we propose a two‐stage methodology that integrates accident clustering with Bayesian inference. Initially, a Gaussian mixture clustering algorithm is developed to categorize accident severity. Subsequently, a Bayesian network is constructed to explore the risk factors associated with accident severity. The proposed model is calibrated and validated using accident data collected from mountainous freeways in Yunnan Province, China, spanning the period from 2016 to 2021. The findings suggest that our proposed accident clustering method exhibits superior robustness compared to alternative clustering techniques. Bayesian inference analysis further elucidates that accident severity is significantly influenced by factors such as driving behavior, weather conditions, and road surface conditions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".