EFFECT OF DARKNESS ON THE CAPACITY OF LONG-TERM FREEWAY RECONSTRUCTION ZONES
Bibliographic record
Abstract
This paper presents an investigation into the effect of darkness on freeway capacity at long-term reconstruction sites. It is part of ongoing research to examine the factors that affect freeway capacity at work zones. Capacity data from two work sites in Ontario, Canada were examined. At each site, capacity observations during the PM peak period were recorded on weekdays before and after the change from Eastern Daylight Time to Eastern Standard Time. Data from video records were then processed using 5-min intervals. Heavy vehicles were converted to passenger car equivalents using the HCM equivalency factors. Study results suggest that darkness has a significant effect on freeway capacity at freeway reconstruction work zones. However, darkness affected capacity differently at the two sites investigated. At one site, the decline in freeway capacity due to darkness was found to be 7.5 % while this decline was found to be only 3.25 % at the other site. The study linked this difference to the effect of grade at the second site. This, in turn, suggests that the compound effect of two or more variables on freeway capacity at reconstruction sites is interactive rather than additive, which is consistent with findings from previous stages of this research. 1.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".