Windshield Glare from Bus Interiors: Potential Impact on City Transit Drivers at Night
Bibliographic record
Abstract
Windshield glare at night is a safety concern for all drivers. Public transit bus drivers also face another concern about glare caused by interior lighting sources originally designed for passenger safety. The extent to which interior light reflections contribute to glare is unknown. Unique methods for measuring discomfort and disability glare during bus driving were developed. An initial simulation study measured windshield luminance inside of a New Flyer D40LF diesel bus parked in a controlled, artificial, totally darkened test environment. Findings indicated significant disability glare (from elevated luminance) in the drivers’ primary field of view due to interior reflections. Any reduction in contrast would result in less prominent glare if actual driving conditions differ. To assess this, levels of windshield glare were also measured with the bus parked on the roadside under the “background glow” of the urban environment. Findings reveal that under road conditions the extent of disability glare from interior reflections is much less, but not negligible, when contrast is reduced. The information gathered in these studies may be useful to manufacturers and transit authorities to improve conditions for drivers and the travelling public. Measurement methods developed in this study may prove useful for assessing elements of interior design. The difference between disability and discomfort glare is discussed. The results provide directions for implementation of glare control strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".