A Method to Calibrate Roadway Lighting Warrants: Case Study of Quebec's Highways
Bibliographic record
Abstract
The Provision of Roadway lighting follows a warrant system established four decades ago. The current state of the practice finds many agencies in North America and the rest of the world simplifying the grid system to contain fewer elements already available to them and custom- tailor scores based on expert criteria. This tendency suggests the need for a method to conduct a local calibration of the warrant’s grid supported by the local crash-history. This paper presents a method to calibrate the scores of the warrant system utilizing only those elements available and significant from a statistical perspective in explaining less frequent and severe night-time collisions. A case study of the province of Quebec in Canada illustrates the application of the method. As explained later, only few factors survive the analysis and were found to be significant in explaining less frequent and severe accidents. Values of such factors were normalized and re-scaled to obtain the scores for grid G1 (highways). The number of lanes, width of the shoulder, density of intersections, traffic volume, and night-to-day crash ratios were calibrated to obtain two grids, one for severity and one for frequency. A modified grid without night-to-day ratios is proposed for new highways. The method to locally calibrate over statistical analyses proposes a strong foundation for lighting decisions better suited from a liability perspective.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".