The use of a naturalistic driving route for characterizing older drivers
Bibliographic record
Abstract
Although the vast majority of older drivers are safe, there are some older drivers who are at risk of crashes due to health-related changes in functional status. For licensing agencies worldwide it is a challenge to identify unsafe older drivers. One form of older driver assessment that can be done conducted is an on-road test. Often this occurs in an unfamiliar vehicle and on roads that are not familiar to the older driver. This could be detrimental to their driving performance and lead to an overestimation of their crash risk. Purpose: The purpose of the current study is to determine whether the route used for the Driving Observation Schedule (DOS), a specific driving task designed to observe and record driving performance, is actually representative of older drivers’ everyday driving in Melbourne Australia. This is a sub-study of the Ozcandrive study, which is a partner study to Candrive. Methods: Older drivers (75+ years old) were asked to describe locations where they typically drive. A route was then devised to incorporate those locations, and the older driver was observed for their driving behaviours over this route. Older drivers’ vehicles were equipped with a device that monitored their driving locations by global positioning system (GPS) technology at 1 Hz. These same older drivers were followed over several months for their everyday driving using the same device. All trips made were compared for their location against the DOS route. These results were then expressed as a percentage of the trips that included a road from the DOS route, in order to determine how representative the DOS route was of each older drivers’ everyday driving. In addition to location, speed patterns were also compared between the DOS route and everyday driving. Results: The average distance of the DOS route was 13.8 ± 5.3 km, and on average it took 31.0 ± 7.6 minutes to drive, for the 23 older drivers that were included in the sample for this study. Over the 108 ± 18 days whereby the older drivers were monitored for their everyday driving, the older drivers drove 2384 ± 1504 km, and made 385 ± 155 trips. The roads that were part of the DOS route represented 9 ± 8 percent of roads that were used during the everyday driving trips. The DOS route and driving was similar to everyday driving in terms of speed limits of the roadways, exceeding the speed limit, and speed of driving. Drivers spent the majority of time driving on roadways that had speed limits of 50 and 60 km/hr (DOS = 80.4%, everyday = 74.1%). There was a slight trend for everyday driving to be on roadways with faster speed limits and have faster driving than DOS driving. Conclusions: These results suggest that a route can be formulated that will be representative of most of the everyday driving of older drivers. Use of such a route has promise for determining the performance of older drivers under conditions which are typical for their everyday driving. Future research that combines driving behaviour observation, crash data, naturalistic driving as well as health and functional testing for individual older drivers will do much to provide more definitive information about this growing cohort of drivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".