Night-time accidents: a scoping study. Report to The AA Motoring Trust and Rees Jeffreys Road Fund
Bibliographic record
Abstract
Context: \nOnly a quarter of all travel by car drivers is undertaken between the hours of \n19.00 and 08.00, but it is in this period that 40 percent of fatal and serious \ninjuries are sustained by drivers. This indicates that car travel at night carries a \ngreater risk of being killed or seriously injured than does travel during the day. \nThe literature indicates that disproportionate numbers of young drivers, \nespecially young men, are injured at night. But to be able to introduce \nmeasures targeted at this group more needs to be known about the purpose of \ntheir journeys, the types of roads they travel on, and how far they drive and at \nwhat times in the evening and at night. \nOlder drivers tend to have fewer accidents at night, but little is currently known \nabout how much can be accounted for by exposure related to their driving \npatterns. People over the age of 60 years form about 20 percent of the \npopulation, yet they make up over a quarter of traffic fatalities. \nThese two groups of young and older drivers have been selected for study with \nthe following aims: \n(a) to assess what information exists which relates to night-time exposure by \nactivity and by group (young and older); \n(b) to assess what is known about exposure and risk to young and older drivers \nat night, in conjunction with an analysis of relevant accident data to provide a \npicture of the size of the potential problem areas, and gaps in current \nknowledge; \n(c) to identify people’s concerns, attitudes and beliefs with regard to the \nproblems of night-time driving; and \n(d) to provide the basis for decision on what measures might be brought to bear \non the problem, and what further research would be needed in order to point to \nfocused action. \nThis scoping study is in two parts and provides an assessment of the \ninformation available and hence the gaps in our knowledge on the nature and \nextent of night-time driving, and the risks involved at these times. The first \npart assesses the available data, and the second uses focus groups to gather the \nviews of drivers themselves, together with their concerns, attitudes and beliefs \nwith regard to the problems of night-time driving. \nThe measurement of exposure, or amount of travel by car, of drivers of \ndifferent age and gender is central to the assessment of the risk of being killed \nor injured in a road traffic accident. In this study, the measure of exposure used \nis distance travelled per person per year. This has been combined with casualty \n data to make preliminary assessments of risk to people of different ages and \ngender of driving at during the daytime and at night.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".