Impacts of road and rail temporal traffic variations on grade crossings exposure, design, and regulation in Manitoba
Bibliographic record
Abstract
Transport Canada has recently published regulations and guidance for design considerations at grade crossings. Cross-product, or the product of average daily vehicles and trains, is one of several criteria that define warning system requirements. While based on readily available data, application of the cross-product may oversimplify the interactions between vehicles and trains at a crossing by failing to account for known temporal variations in both modes. Through two analyses, this paper investigates the effects of temporal traffic variations on estimated grade crossing exposure and develops insights about alternatives to quantify this exposure. The first analysis considers the effect of daily road traffic variations on grade crossing exposure and compliance at 240 rural grade crossings. Nine of the studied crossings (4%) experienced at least one day for which the estimated single-day exposure indicated a need for an upgraded warning system, based on the cross-product criterion alone. Conversely, 91 crossings (38%) had warning systems that would be considered over-designed for the entire year. The second analysis considers the effect of hourly road and rail traffic variations on grade crossing exposure at 13 urban grade crossings by estimating hourly cross-product equivalents. Each of the eleven studied gated crossings featured at least seven hourly cross-product equivalents that exceeded the cross-product threshold for gated crossings. The findings demonstrate that the cross-product may misrepresent vehicle-train interactions at a crossing by suppressing temporal variability in road and rail traffic. Consequently, these variations should be considered in design and prioritization decisions for reducing risk and delay at grade crossings.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".