Justifying and Prioritizing Roadway Lighting: A Case Study of \nQuebec Highways
Bibliographic record
Abstract
ABSTRACT \nJustifying and prioritizing roadway lighting: A Case Study of Quebec Highways \nMahnoush Heydari \n \nRoadway lighting is an effective countermeasure capable of reducing night-time motorized collisions under the right circumstances. Its initial viability can be learnt through collision modification factors showing beneficial effects of roadway lighting on local roads. However, this requires time-series of data from several years before and after the implementation of lighting. There is a need to estimate collision modification factors of lighting from locally observed cross sectional data from as little as one year. There is also a lack of a practical method capable of replacing the complicated warrant system to support decisions of whether or not to illuminate roads. Such method should be able to identify and prioritize segments that will benefit the most from being illuminated. This research presents a method to estimate collision modification factors with as little as one year of data. In addition, this research presents a practical method that identifies and prioritizes candidate road segments for being illuminated. A case study of Quebec's highways found that lighting is an effective countermeasure and that expected benefits approximate 60% reduction in night time collisions. It was found that segment size plays an important role and that Bayesian data fusion can be used to abstract from segment size to estimate a generic collision modification factor. It was found that safety performance functions for desired land use and sites type can be used in combination with the observed number of collisions to classify those sites expected to observe benefits from being illuminated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".