Quantifying the Effects of Traffic Calming Devices on Noise Levels
Bibliographic record
Abstract
Traffic-related noise pollution is a major environmental stressor causing various adverse health impacts on humans. Road traffic noise levels are influenced by the type of vehicle, tire-to-pavement friction, and driving style. Traffic calming devices like speed humps and speed tables play a significant role in affecting the overall operational factors of vehicles, whereas the major contributors of pollutants are caused due to the abrupt deceleration, braking, and acceleration of vehicles while passing over them. This paper aims to quantify the effects of different traffic calming devices on the noise generated by the traffic flow. To compare the noise emissions, the noise levels of a particular vehicle passing at and after the traffic calming devices were measured simultaneously while maintaining most of the site characteristics and traffic data similar. This research will ascertain statistical analysis of the noise levels emitted by vehicles at the traffic calming devices. The 24 traffic calming devices selected for this study included 12-speed humps and 12-speed tables for 2-lane and 4-lane at 3 different zones (residential, school, and industrial) in multiple areas in Doha City, Qatar. The data collection conducted for 8-hours per site showed that the observed mean noise levels for all the sites exceeded the WHO standards [53 dB(A)] and Qatar standards [55 dB(A)] permissible noise levels because of the vehicle fleet mix. The analysis indicated that traffic calming devices generated comparatively higher noise than the control point, meanwhile speed humps emitted more noise levels than speed tables. Further, it was statistically proven that traffic calming devices in 4-lane emitted higher noise levels than those in 2-lane. In addition, the industrial zone was observed to generate higher noise levels than the residential and school zone.
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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 | 0.001 | 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".