Analysis of Work-Zone Crashes Using the Ordered Probit Model with Factor Analysis in Egypt
Bibliographic record
Abstract
Work-zones, due to their nature, are predisposed to hazardous situations. This is a consequence of conducting construction work within the vicinity of, or near, vehicular traffic. The exposure to danger underscores the need for proper understanding of the occurrence of work-zone crashes, as well as the risk factors associated with them. This paper aims mainly to develop a hybrid approach that combines a factor analysis method and an ordered probit model to carry out a comprehensive analysis of work-zone crashes. The paper presents an analysis of work-zone data crashes from 2010 to 2015 that occurred in Egyptian long-term highway maintenance and rehabilitation projects. Factor analysis is used to identify the main and common factors that influence work-zone crashes and to calculate the weight of every factor. The ordered probit model is developed, based on the results of factor analysis scores, to examine the contribution of common factors in the severity of work-zones. The most influential factors that have contributed to work-zone crashes are weather condition, number of lane closures, type of surface construction, road character, day of week, and so forth. In addition, the results indicated that four common factors are significantly affecting the severity of work-zone crashes in Egypt.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".