REDUCING UNINTENTIONAL CROSSING OF THE CENTER LINE OR EDGE LINE. LITERATURE REVIEW.
Bibliographic record
Abstract
Head-on collisions and run-off-the-road accidents caused 57% of all personal injuries sustained in fatal accidents on public paved roads during 1997-2001 in Finland. A drowsy or inattentive driver is often behind these accidents. This literature review searched for means to improve drivers' lane keeping with different kinds of road markings. The potential number of deaths which center line measures could affect is about 60 in Finland annually. The corresponding numbers for edge line measures is about 10. The safety effect of rumble strips has varied in different studies conducted in the USA. According to Griffith (1999) some big percentual reductions of up to 70% in the number of accidents after the introduction of rumble strips may be due to methodological problems in the studies (after-period too short, no control sites). The shoulder rumble strips are more likely to reduce accidents by 20-30% on motorways. The safety effect of center line rumble strips on two-lane roads is not well known. An important result in this respect comes from Norway, where Giaver et al. (1999) found that raised rib markings on the center line had a greater safety effect than on the edge line. However, the road geometry in Norway may contribute this result. In any case, the positive experiences with profiled road markings have led to fairly widespread recommendations on the use of such markings on edge lines or road shoulders in Norway, Canada and the USA. The measures to prevent unintentional crossing of the center line or edge line can be divided into three groups according to how crossable they are: 1. Easily crossable (visual effects, profiled road markings, road studs); 2. Crossable at low speed (humps, flexible bollards, traffic islands); 3. No crossing possibility (guard rails; these were not included in this study). Narrowing the lane width by some center line measure seems to be an effective means to improve safety because the driver has to pay more attention to the driving task. At the same time the distance between opposite flows of traffic can be increased, which in turn should reduce the risk of collision. When the lane width is narrowed vehicle speeds usually drop, and variations in the lateral position of the vehicles are reduced. However, according to a Dutch study (Ward et al. 1995) this does not lead to overloading of the driver's task.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".