Assessment of Traffic Noise Level: A Case Study of a Residential Neighbourhood
Bibliographic record
Abstract
The World Health Organization (WHO) has classified ambient noise as hazardous pollution that has negative psychological and physiological consequences on human health.Motorized vehicles are a major cause of noise pollution.It is a global phenomenon that has developed to become a major source of concern for the general public and governments alike.The purpose of this research is to look at traffic noise levels during peak and off-peak hours, as well as contributing elements like traffic volume, heavy-vehicle speed, and meteorological parameters like temperature, humidity, and wind speed, in a specific residential neighbourhood in Kuwait.This study focused on three types of roadways: expressways, main arterial routes, and collector streets.Other causes of noise were also investigated.All measurements were taken simultaneously.The findings revealed that traffic noise levels on all the identified types of roads exceeded the permitted limit.The average equivalent noise levels (LAeq) on the expressway, major arterial road, and collector street were 74.2 dB(A), 70.47 dB(A), and 60.84 dB(A), respectively.Furthermore, a positive correlation coefficient was found between traffic noise and traffic volume, as well as traffic noise and the 85th percentile speed.However, there was no significant relationship in metrological parameters.Abnormal vehicle noise caused by inadequate maintenance or user-enhanced exhaust noise was identified as one of the most significant variables influencing total traffic noise measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".