Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise
Bibliographic record
Abstract
Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>W</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) and subsequently the noise level at receiver (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) were estimated by “ASJ RTN-Model.” Having estimated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>W</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>S</mml:mi><mml:mi>E</mml:mi><mml:mi>L</mml:mi></mml:math>) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".