Effect of traffic characteristics and road geometric parameters on developed traffic noise levels
Bibliographic record
Abstract
The main objective o f 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 o f the intersections approaches, including number and width o f 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 o f the main factors controlling traffic noise levels.Results o f 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 o f 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.s o m m a ir e L'objectif principal de cette étude était d 'évaluer les facteurs importants qui peuvent influencer les niveaux de bruit du trafic aux intersections routières comportant des feux de signalisation.Pour atteindre cet objectif, les niveaux de bruit du trafic et les facteurs susceptibles de les affecter ont été mesurés à 40 intersections.Les niveaux équivalents, maxima et minima ont été mesurés pendant des périodes de 1 minute, incluant l 'intervalle de temps où le feu était vert.Le volume de trafic ainsi que sa composition ont été enregistrés à l 'aide d 'une caméra vidéo, alors que la vitesse a été mesurée à l'aide d'un radar.Les paramètres géométriques d 'approche des intersections, incluant le nombre et la largeur des voies, la largeur des approches et la pente ont été répertoriés.De plus, la texture de la surface du pavage a été évaluée selon le « pedulum » britannique.Les données ont été analysées dans le but d 'évaluer les facteurs principaux qui contrôlent les niveaux de bruit du trafic.Les résultats indiquent que les niveaux de bruit équivalents dépendent principalement du volume de trafic, alors que les niveaux maxima sont plutôt attribuables aux nombre de poids lourds qui empruntent l 'intersection et à l'effet des klaxons.Par ailleurs, les niveaux minima sont surtout reliés à la texture de la surface de pavage.Lorsque les niveaux de bruit à des distances variables de la ligne d 'arrêt sont considérés, la vitesse du trafic s'avère avoir un effet significatif sur les niveaux équivalents de bruit.Vol. 32 No. 4 (2004) -
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".