Effect of traffic characteristics and road geometric parameters on developed traffic noise levels
Bibliographic record
Abstract
The main objective of this study was to evaluate the major factors affecting traffic noise levels at signalized intersections. To achieve this objective, traffic noise levels and the factors expected to affect it were measured at 40 signalized intersections. Equivalent, maximum, and minimum noise levels were measured during one minute interval including the green time interval. The traffic volume and composition was taped using a video camera, while the traffic speed was measured using speed radar. The geometric parameters of the intersections approaches, including number and width of driving lanes, approaches width and slope, were collected. Also, pavement surface texture was evaluated using the British pedulum. The collected data was analyzed to evaluate the effect of the main factors controlling traffic noise levels. Results of the analysis indicated that equivalent noise levels are mainly dependent on traffic volume, while the maximum noise levels were found to be dependent on the number of heavy vehicles passing through the intersection and horn effect. On the other hand, the minimum noise levels were mainly dependent on pavement surface texture. When noise levels at different distances from the signal stop line were considered, traffic speed was found to have a significant effect on equivalent noise levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".