Evaluation of the effectiveness of auditory speeding warnings for commercial passenger vehicles –a field study in Wuhan, China
Bibliographic record
Abstract
Auditory warning of speeding behaviour is considered to be one of the most effective methods developed to reduce the accidents involving commercial passenger vehicles. Facing a complex, mixed traffic condition and a lot of risky driving behaviours in China, commercial passenger vehicles need an effective speeding warning system to reduce the high accident rate. Although many automobile manufacturers have installed the speeding warning systems on their vehicles, the styles of these auditory speeding warning systems are different, and few studies has been found to investigate the effectiveness of the auditory speeding warning systems for commercial passenger vehicles. Therefore this study is intent to fill such a gap to evaluate the effectiveness of three different sound‐based speeding warning styles. In this study, thirty drivers qualified for driving the commercial passenger vehicles are recruited and then asked to drive for four 80‐km field trips on an expressway in Wuhan, China. Driving behaviour is logged by a monitoring system and is monitored by two observers during these trips. Study results showed that ‘beep warning’ is most effective and ‘break‐sound warning’ is the least. Basically, the results of this study could provide a good reference for development of future voice‐based speed warning systems in China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".