Involvement of older drivers at crashes at unsignalised and signalised intersections
Bibliographic record
Abstract
By the year 2044 the population aged over sixty in Australia is predicted to double. The consequences of this increase include a greater cost to the community in road trauma. It is known that elderly drivers have the highest crash rates at intersections, are most likely to be at fault in a crash and have the highest mortality rate in accidents. The cost of crashes will therefore increase disproportionally to the increase in elderly drivers on the road. This research project identifies the likely ways that elderly drivers will cause increased road trauma, based on data from the Toowoomba area compared to that from the rest of Queensland. Toowoomba already has a higher than average proportion of elderly drivers on the road in Queensland, and so it provides an indicator of likely future trends for the rest of the state. \nThe dissertation quantifies historical trends in intersection crash rates for elderly drivers in Toowoomba and shows how these trends compare to the rest of Queensland and a comparable overseas country (Canada). The causes of accidents in which elderly drivers feature are discussed and include issues such as physical frailty, skill levels and road hazards. The road hazards investigated include traffic control, intersection types, traffic flow, weather conditions, available lighting, pre collision actions and the affects of common driver behaviours. The project concludes that roundabouts are a major frustration for elderly drivers, and one of the most common features in crashes. Our senior drivers also feature prominently in crashes at unsignalised cross intersections. \nA major conclusion is that the State should attempt to make unsignalised road intersections friendlier for drivers over sixty, and that this could be done by reducing the number of decision points required when navigating the intersection. For heavily trafficked intersections the use of traffic signals remains the safest design feature for such drivers. It is also argued that there is a need for increased education for older drivers on the need to maintain driving skills and to adapt to their changing \nphysical capabilities. These programs would best be delivered through workshops and seminars at retirement villages and supermarkets where elderly people tend to be concentrated. Modern communication technologies, based on internet delivery, are not likely to be widely used by the target audience.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".