Experiences with GPS Travel Diaries in Rural Older Driver Research
Bibliographic record
Abstract
This paper describes using passive Global Positioning Systems (GPS) data collection and Geographic Information System (GIS) with participant prompted recall to study the travel habits of rural older drivers. It is based upon the research of a convenience sample of 60 rural drivers (29 men, 31 women, average age 69.6 years) in New Brunswick, Canada. The transportation needs of a growing population of older rural residents, many who face the risk of not being able to meet their needs if they can no longer drive, are not well understood and represent an immediate and future policy need. GPS-based travel diaries are a useful method to obtain origin/destination and other contextual information in support of rural transportation planning. A total of 1,649 “stops” (periods of non-movement lasting 1 minute or more) by participant vehicles were recorded with the GPS units. Approximately 8% of all “stops” were due to stoplights or traffic delay. The remaining “stops” were organized into 1,494 trips (one origin with one destination), with participants supplying purposes, who was driving, and passenger details for 99.1% of recorded trips. Travel data were collected on average for 5.3 days per participant. An external battery for the GPS unit minimized the typical satellite acquisition but was exhausted in 10% of cases. Only 2.2% of recorded trip ends were due to lost reception or acquisition delay and in each case the missing distance data were interpolated. Service clubs and snowball sampling were the most effective means of recruiting rural participants.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".