Time Lost by Driving Fast in the United States
Bibliographic record
Abstract
BACKGROUND: Motor vehicle drivers make decisions about speed while traveling and thereby trade off a potential saving from shorter travel time (if the trip is uneventful) against a potential loss of time (if the trip results in a crash). METHODS: The authors used computerized modeling based on national data to examine the benefits from small changes in average driver speed on public health in the United States. Lost time due to both travel and crashing was calculated, along with optimal speed to minimize net time lost. RESULTS: The baseline analysis suggested that 1 hour spent driving was associated with approximately 20 minutes of additional lost time in life expectancy due to the potential of a crash. A approximately 1-km/h (0.6-mph) increase in speed for the average driver yielded a approximately 26-second approximate increase (not decrease) in total expected lost time because the savings from reduced travel time were more than offset by the increased prospect of a crash. A 3.0-km/h (1.8-mph) decrease in average driving speed yielded the least amount of total time lost (95% confidence interval [CI]: 2.5-4.1 km/ h [1.5-2.5 mph]). This speed yielded about 11,000 fewer crashes each day, saved about 3.6 hours per year for the average driver (95% CI: 2.0-6.2 hours), and conserved about 199 cumulative life years for society annually. CONCLUSIONS: As a nation, drivers in the United States travel slightly too fast and could improve overall life expectancy by decreasing their average speed slightly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".