A structured model for evaluating countermeasures at highwayrailway grade crossings
Bibliographic record
Abstract
Various countermeasures can be introduced to reduce collisions at highwayrailway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highwayrailway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highwayrailway grade crossing, collision prediction model, countermeasures, Poisson regression.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".