Effect of Winter Weather and Road Surface Conditions on Macroscopic Traffic Parameters
Bibliographic record
Abstract
This paper presents an empirical study focusing on identifying the main factors that affect the capacity and free-flow speed (FFS) of urban freeways under inclement winter weather conditions. The weather and road surface condition factors examined include air temperature, wind speed, hourly snow intensity, visibility, snow on ground, and road surface condition describing the road slipperiness caused mainly by snow events. Data on traffic operations and the associated weather and road conditions observed at two freeway locations over the 2010–2012 winter seasons were used in an extensive statistical analysis. Linear regression models were calibrated for both capacity and FFS reductions as related to various weather and road condition variables. It was found that visibility and road surface conditions had a statistically significant effect on both capacity and FFS. Snow intensity was found to be significant only when the visibility factor was excluded; this finding suggests a refutation of these two factors on capacity and FFS. The modeling results were compared with those recommended by the Highway Capacity Manual 2010, showing that, in many cases, the manual could underestimate or overestimate the effects of winter weather conditions and that the proposed models provided a more reasonable estimate at a higher level of granularity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".