Spatiotemporal Evaluation of Injury Severity of Expressway Rear-End Crashes in China: Insights Using Random Parameters Approaches
Bibliographic record
Abstract
Rear-end crashes have become an urgent problem in China, resulting in severe casualties and massive property damage. This study aims, first, to discover the determinants affecting injury severity in expressway rear-end crashes and to examine the corresponding changes over time and space, and, second, to identify the sources of unobserved heterogeneity shifts, which could have substantial implications for efficient and effective crash prevention. Using three-year crash data (2017 -2019) from two expressways with speed limits of 120 km/h and 100 km/h (G2 and G25) in Jiangsu and Guangdong provinces in China, four random parameter logit model (RP-LM) approaches were used in this study to analyze the contributing factors: fixed parameter LM, RP-LM, RP-LM with heterogeneity in means, and RP-LM with heterogeneity in means and variances. With three injury severity consequences (severe injury, minor injury, and no injury), the characteristics of the driver, vehicle, roadway, environment, and others were proposed as possible determinants. The temporal and spatial stability were then explored using transferability tests. The marginal effects were computed to explore further potential heterogeneity and spatiotemporal variations. The estimated results indicate the superiority of the proposed model over its base counterparts, with very good [Formula: see text] values all over 0.64. Saturday, early morning, and winter indicators were identified as significant random parameters, and several variables were observed to manifest heterogeneity in means and variances. Speeding behavior, early morning, and heavy truck indicators increased the likelihood of minor and severe injury. These findings could significantly improve expressway safety related to rear-end crashes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".