Fifteen-Passenger Vans and Other Transportation Options: A Comparison of Driver, Vehicle, and Crash Characteristics
Bibliographic record
Abstract
OBJECTIVE: Fifteen-passenger vans (15-PVs) are a convenient and economical way to transport small groups of people and many educational, community, and health organizations utilize them. Given recent tragic crashes involving 15-PVs, many organizations are reconsidering their use. The goal of this study was to examine driver, vehicle, and crash characteristics of fatal 15-PV collisions over the past 2 decades in comparison to 3 other common vehicle classes. METHODS: We used data from the Fatality Analysis Reporting System (years 1991-2008). Driver, vehicle, and crash characteristics were compared by vehicle classes (15-PV, cars, minivans, and intercity buses) using proportions along with odds ratios (using cars as the reference category) for dichotomous variables and means and mean differences for continuous variables. Logistic regression and analysis of variance were used to statistically compare odds and means, respectively. The odds and absolute risk of a first, subsequent, and either rollover by vehicle type and occupancy rate were also examined. Odds and absolute risk of a rollover event by occupancy rate were calculated. RESULTS: Compared to car drivers, van drivers typically had a better past 3-year driving record. Van drivers performed significantly fewer actions suggesting aggressive driving (e.g., speeding). However, the proportion of van drivers who were deemed to have followed improperly or to have overcorrected was greater. A vehicle rollover was cited almost twice as frequently in van crashes compared to other passenger vehicles. Of the 4 vehicle types studied, all were more likely to rollover as their occupancy rates increased. Fully loaded 15-PVs had almost 13 times the odds of rollover compared to fully loaded cars. Minivans when full (7 occupants), often seen as the replacement for 15-PVs, were found to have over 3.5 times the odds of rollover of fully loaded cars. CONCLUSIONS: Drivers need to be aware that as occupancy rates of the vehicles they drive rise so does the risk of rollover and fatalities, especially among minivans and 15-PVs. Organizations transporting groups need to balance cost and safety management by selecting vehicle types and drivers with acute awareness of the risks involved.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".