Instrumentation to Measure On-Road Cyclist Noise Exposure: Considerations for Study Design with Smartphones and Sound Level Meters
Bibliographic record
Abstract
Cyclist noise exposure has implications for health, comfort, and safety. Methods used for in situ measurement of on-road noise levels for cyclists vary, and the effects of key study design factors have not been investigated. To enable reliable research into cyclist noise exposure, this study aims to determine the accuracy of smartphone noise measurements in comparison with a sound level meter (SLM) reference instrument, and how noise levels are affected by travel speed, air speed, sensor placement, and use of a windscreen. Field data were collected with paired instruments in a typical urban cycling scenario, and comparisons made varying one design factor at a time (smartphone versus SLM, with versus without windscreen, handlebar versus shoulder placement, etc.). Results show that smartphones can generate reliable measurements (compared with SLM) of high-resolution (1-s) cyclist exposure for C-weighted noise, but not A-weighted noise. Sensor placement and windscreen have small effects on noise readings, but air speed and travel speed greatly affect measured noise levels. Future studies measuring on-road noise must consider the effects of wind- and bicycle-generated noise to ensure internal validity. Studies should also consider both study objectives and instrumentation when selecting a noise measure (frequency weighting). Research is needed into bicycle noise generation and perception of traffic noise by cyclists to enhance the reliability of future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".