Assessing Mileage Exposure and Speed Behavior Among Older Drivers Based on Crash Involvement Status
Bibliographic record
Abstract
ABSTRACT Population and crash projections for the year 2030, suggest that drivers age 65 and older will represent one quarter of total population and one quarter of motor vehicle-related fatalities. The full implications of these trends in terms of crash reduction measures such as operator education, self-regulation, and licensing regulations are unknown. However, for any of these crash reduction measures to be effective, it is first imperative to understand and identify older driver behavior (or activity patterns). Only then can data be linked with crash involvements to determine effective countermeasures allowing safe mobility for older persons. This study investigates the driving patterns of seniors who have and who have not experienced a crash during a 14-month study period using the longitudinally collected GPS trip data. This investigation allows for an empirical investigation to determine if older drivers with a recent crash experience drive differently in terms of speed, time of day, or roadway types. This study found that crash-involved older drivers usually traveled longer distances and traveled at higher speeds than older drivers who were not involved in crashes. While travel on freeways between the two groups showed significant mileage and speed differences, the crash-involved older drivers were more likely to exhibit over-speeding activity at arterials and local roadways than drivers who were not involved in crashes. This study suggests that transportation safety engineers and policy makers should also aim speed campaigns to older drivers. Traditionally, older drivers have not been a target population for these types of campaigns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".