Method for Road Lighting Audit and Safety Screening at Urban Intersections
Bibliographic record
Abstract
The importance of road lighting in improving nighttime safety is evident; however, the lack of actual field measurements of illuminance results in a gap in knowledge about the adequacy of installed road lighting for clear nighttime visibility. Previous studies have considered the effect of the presence or absence of road lighting on safety, but few have measured actual illuminance. This study tested a uniform method for performing a simple road lighting audit and safety screening for any area. To perform the proposed audit, a photometric sensor, data logger, and information on the city lighting standards, georeferenced accident data, and traffic flow data were used. To collect field measurements, data collectors crossed each side of an intersection with a sensor. On the basis of the collected data, the following values were calculated: the average illuminance of each approach to an intersection and of the whole intersection and the uniformity ratio of the intersection. These results were used to compare the intersection illuminance with the city lighting standard to see if the installed road lighting was performing adequately. This method was applied to a case study in Montreal, Quebec, Canada, where the lighting at 59% of the selected sample intersections was found to be substandard. Statistical analysis showed that the number of night accidents was correlated with traffic flow and substandard illuminance. The factors contributing to average illuminance were clear sky, hour of night, and presence of light poles and commercial lights.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".