Trip-Based Measures of Naturalistic Driving: Considerations and Connections With Cognitive Status in Older Adult Drivers
Bibliographic record
Abstract
Older adult drivers can experience age-related health declines, particularly cognitive health, that can negatively impact driving performance and lead to driver license revocation. The measurement of naturalistic in-car driving behavior can be used to evaluate trip complexity, including diversity, length, and frequency of trips and associated destinations. This paper examined measurement methods for trip complexity and driving destinations using GPS data for older adult drivers with differing health statuses, focusing primarily on cognitive health status. Older driver subgroups included four groups with relatively stable health: better overall health, better cognitive health, worse cognitive health, and worse overall health and one group with declining cognitive health. Older drivers with better health status (overall and cognitive) had higher measured trip complexity compared to those with worse health status. Two variables, mean trip distance and percent of trips driven during the workweek evening rush-hour, declined significantly (p ≤ 0.049) in-line with cognitive declines but did not meaningfully discriminate cognitively declining older drivers from cognitively stable drivers (sensitivity: 18.8%-43.8%; specificity: 58.0%-93.3%). This contrast between significant group differences and nonpredictive declining group changes may suggest that self-referential naturalistic driving measures are needed to identify meaningful changes in driving behavior. In addition to the development of self-referential driving measurement systems, careful consideration of big data analysis as they apply to naturalistic driving is warranted. These include but are not limited to issues of validation, anonymity, measure-based definitions, and occurrence of outlier-type driving events like city-to-city travel.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".