Effects of Traffic Density on Measured Sound Levels – A Case Study
Bibliographic record
Abstract
In road noise prediction algorithms/models, it is commonly accepted that the sound emission from a road segment (the cumulative sound output from a stream of moving vehicles) increases at a rate of 3 decibels for each doubling in the number of vehicles passing the measurement point within a given period of time. In essence, this corresponds to a doubling of the emitted sound energy for a doubling in the vehicle count. This relationship is intuitively correct and has been proven accurate for various road noise models in use today. This results in a daily sound emission pattern which typically peaks during the busiest hours of the day (morning and afternoon “rush” hours) and is lower during those hours for which traffic is reduced. However, consider the resultant daily sound emission pattern when the vehicular density commonly exceeds the critical density for a given roadway. By way of case study, this paper examines a 72-hour measurement window at receptor points adjacent to the Don Valley Parkway in the City of Toronto – a roadway which commonly exceeds the critical vehicular density. The study examines the true daily sound emission pattern for this specific traffic scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".