Introducing Road Safety Audits in Kuwait: Kabd Road Case Study
Bibliographic record
Abstract
An In-Service Road Safety Audit (RSA) project was commissioned by the Ministry of Public Works (MPW) in Kuwait, which would be the first RSA to be completed in Kuwait. The project was intended to serve as a demonstration project, highlighting the concept and process of a road safety audit, in an attempt to illustrate the benefits of RSAs and how RSAs may improve the overall level of safety for the road users in Kuwait. For Kuwait’s inaugural RSA Project, a roadway known as Kabd Road was selected. The roadway, which is perceived to be very dangerous and often referred to as the ‘Death Road,’ is located south of the metropolitan area of the City of Kuwait. Kabd Road was considered to be an excellent project to demonstrate the RSA concept since the roadway has some design features that adversely impact the safety performance. In addition, the road appeared to be poorly maintained at some locations, which also created some significant safety hazards for motorists using the roadway. Poor driver behavior of the motorists using Kabd Road would also a contributing factor to the dangerous conditions that exist on the roadway. This paper describes the demonstration project, including the process that was followed to implement an RSA in a region where RSA did not previously occur. The paper also provides some of the findings from the RSA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".