Level of Service of Safety Revisited
Bibliographic record
Abstract
The concept of the level of service of safety (LOSS) was developed at the Colorado Department of Transportation in 2000. LOSS reflects how a roadway segment or an intersection is performing in reference to the expected frequency and severity of crashes predicted by its safety performance function. The LOSS concept provides quantitative assessment and qualitative description of the degree of safety of a segment or an intersection. In addition, the loss concept facilitates effective communication about safety problems to other professionals, the traveling public, and elected officials. The LOSS concept was first introduced in a paper titled “Level of Service of Safety: Conceptual Blueprint and Analytical Framework,” published in 2003 in the Transportation Research Record: Journal of the Transportation Research Board, No. 1840. LOSS was incorporated into the first edition of the AASHTO Highway Safety Manual and is used by the Departments of Transportation of Colorado, Louisiana, Montana, Oklahoma, and Wyoming and the Ontario Ministry of Transport, in Canada. LOSS lends itself well to the safety decision-making process in departments of transportation. However, the concept did not initially address correction for the regression to the mean bias. A new method is introduced for using LOSS in concert with correction for regression to the mean bias with an empirical Bayes procedure. In addition, distributional assumptions associated with LOSS are revisited, how the LOSS boundaries are calibrated in the population corrected for the regression to the mean bias is explained, and an intuitive percentile-based reporting method is provided. Finally a diagnostic example and a before-and-after study demonstrating the value of LOSS in identifying the safety problem at a real location are worked through.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".