Bus Noise and Vibration Scenarios
Bibliographic record
Abstract
This research presents insights into the impact of vibroacoustic factors in conventional and electric buses by investigating seven factors: average speed, bus age, road conditions, road network, bus operating days, times, and occupancy levels. Data on these factors and bus vibroacoustic levels were collected through observations alongside vibroacoustic apps (Sound Meter Pro and iDynamics) from 31 conventional and 12 electric buses in Montreal, Canada. The data was analyzed using Pearson's correlation, and predictive models were generated using multilinear regression to assess 650 sub-scenarios for each bus type. The results indicated that electric buses had better vibroacoustic performance compared to conventional buses under different contexts. Furthermore, the factors of age, road conditions, and average speed had notable but varying effects on both bus noise and vibration levels. Thus, this study suggests that adopting electric buses, reducing the age of existing bus fleet, improving road infrastructure, and lowering operation speeds can effectively minimize bus noise and vibration for better environmental and rider comfort. Future research can be adopted to consider a broader range of variables, including maintenance and traffic density, to further refine the effects of these factors on conventional and electric bus vibroacoustic levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".