Safety measures at railway level crossings for pedestrians and cyclists. www.ictct.org/ workshops/03-Vancouver/vanderhorst.pdf
Bibliographic record
Abstract
To improve the safety at railway level crossings in the Netherlands, a number of safety-improving measures has been implemented. Commissioned by ProRail (formerly Railinfrabeheer B.V.), TNO Human Factors conducts studies to determine the effectiveness of these measures with respect to road users ’ behaviour. Among other things, the measures included the separation of two-way traffic by solid white centre lines or physical medians, and separation of slow and fast road traffic at the railway level crossing. At several railway level crossings behavioural video-observations were conducted on the spot in a before-and-after study design. The video-recordings were analysed quantitatively by determining the speed of passenger cars and by conducting a time analysis of non-stopping/stopping behaviour of pedestrians and bicyclists. The behavioural analyses also included the rating of conflicts between road users. Most measures as implemented at railway level crossings improve the behaviour of pedestrians and bicyclists. Within built-up areas (speed-limit 50 km/h), both a solid white centre line and a physical median to separate two-way traffic at the railway level crossing reduce the speed with about 2.5 km/h. The centre line does not prevent slalom manoeuvres or centre-line crossings, whereas a physical median reduces the number of line crossings as well as the severity of bicycle-car overtaking conflicts. Removing slow traffic from the carriageway decreases both the number and the severity of car-bicyclist conflicts and seems to be safety-effective. It also appears to be effective to separate a nearby bicycle-path/road intersection further from a railway level crossing. Twelve metres further away helps already a lot.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".