Evaluating Factors Affecting Safety at Highway-Railway Grade Crossings
Bibliographic record
Abstract
Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. Existing improvements to crossings include the installation of flashing lights or gates, the addition of extra warning devices such as four-quadrant barriers or wayside horns, and the enforcement of speed limits on the approaching highway. Statistical models are needed to ensure that countermeasures introduced at a given crossing are both cost-effective and practicable. However, in large part because of issues of colinearity, poor statistical significance, and parametric bias, many existing statistical models are simple in structure and feature few statistically significant explanatory variables. Accordingly, they fail to reflect the full gamut of factor inputs that explain variation in collision frequency at individual crossings over a given period of time. Before statistical models can be used to investigate the cost-effectiveness of specific countermeasures, models must be developed that more fully reflect the complex relationships that link a specific countermeasure to collision occurrence. This study presents a sequential modeling approach based on data mining and statistical methods to estimate the main and interactive effects of introducing countermeasures at individual grade crossings. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the model in decision support.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".