Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes
Bibliographic record
Abstract
Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.
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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 | 0.000 |
| 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".