Effects of a Feedback/Reward System on Speed Compliance Rates and the Degree of Speeding during Noncompliance
Bibliographic record
Abstract
Speeding is known to contribute to crash risks and severities. One approach to inhibit speeding is to use technology to monitor driver speed and provide drivers with feedback. This paper investigates the effects of a feedback/reward system on speed limit compliance rates as well as the degree of speeding during noncompliance. Data utilized in this research were collected from 37 participants (20 to 70 years old) through a field trial commissioned by Transport Canada. In this field trial, a feedback/reward system was investigated, which provided feedback and rewards to the drivers based on speed limit compliance and safe headway maintenance. The trial consisted of three phases: baseline (two weeks), feedback (twelve weeks), and post feedback (two weeks). Real-time feedback was provided on an in-vehicle display. During the feedback phase, participants also accumulated reward points and could view related information on a special website. Results suggest that feedback increased speed limit compliance, and this positive effect, although dampened, was still apparent even after feedback removal. Moreover, when considering cases with no lead vehicle ahead, the positive effects persisted for high speed limit zones (70, 80, 90 and 100 km/h). In general, when the drivers were noncompliant, the degree of speeding was reduced by the presence of feedback.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".