Effects of Rain on Traffic Operations on Florida Freeways
Bibliographic record
Abstract
Although the correlation between traffic variables and weather appears to be intuitive, quantifying the effects that weather, especially rain, has on driver response in travel speeds and traffic demands is needed to evaluate practical aspects of traffic operations. Previous studies have researched driver responses to inclement weather on freeways located in northern regions of the United States and Canada. However, driver familiarity with local weather conditions is a factor that should be considered in determining inclement weather effects on traffic variables. The focus of this research was to examine driver response to rain precipitation on freeways located in the southeastern regions of the United States to determine whether results from previous studies were general indicators or location specific in nature. To study the impacts of rain precipitation on hourly mean speeds and traffic volumes, hourly weather data and traffic sensor data were collected for two freeway segments in Jacksonville, Florida. The study investigated conditions such as wet versus dry (rain or no rain) and dry versus rain intensity (no rain or light, moderate, or heavy rain) for each segment. The results indicated that mean travel speeds decreased during rainfall events and speed reductions increased with increasing rain intensity. Reductions found for light rainfall events were within the range of previous studies; however, speed reductions during moderate to heavy rains varied widely. The results also indicated that the hour of the day was a factor in the degree of motorists’ speed reduction. Traffic volumes also declined during rainy conditions, with significant reductions during peak hours.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".