Human Factors Issues of Accidents at Passively Controlled Rural Level Crossings
Bibliographic record
Abstract
Collisions between road vehicles and trains at passively controlled level crossings, where no indication is given to drivers of the presence (or absence) of an approaching train, raise important human factors safety concerns. A database search of Canadian level crossing crash data for the 10-year period between 2003 and 2012 and a targeted review of the scientific research literature were conducted to explore the human factors and related risks involved in crashes of this nature. Accidents at passively controlled, rural level crossings where the driver of the road vehicle did not stop constituted 15% of all level crossing crashes. Statistical analysis revealed that this type of accident was most likely to occur in prairie provinces, during daylight hours, and to involve a disproportionate number of heavy vehicles. Nine human factors issues were identified. Four affected driver detection of an approaching train, including sightlines, train conspicuity, unchanging retinal image, and train horn audibility. Five issues related to driver decision making, including looked-but-failed-to-see errors, faulty activation of schema–mental model, distraction, impairment, and information processing. These issues were overlaid across four previously proposed driver approach zones at passive level crossings. Analysis of countermeasures designed for application within the approach zone, which began at the decision sight distance point, revealed several potential countermeasures, including the observation that current minimum sightline guidelines for passive level crossings might not have been adequate to ensure that drivers have sufficient time to assess the threat posed by an approaching train from both directions and to respond appropriately.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".