Evaluating Pedestrian Accessibility at Level Railroad Crossings
Bibliographic record
Abstract
Level railroad crossings offer a unique safety and design challenge for transportation professionals as they accommodate two distinctly different types of infrastructure and represent points of conflict between trains, vehicles, and pedestrians. This paper identifies pedestrian accessibility concerns at level railroad crossings and develops a site assessment tool for practitioners to use in evaluating the accessibility performance of existing crossings. Pedestrian accessibility concerns are design, operational, or maintenance aspects of the transportation system that limit their ability to travel. At a level railroad crossing, accessibility concerns can occur on the approaches, at the crossing surface over the tracks, and with the operational devices. Accessibility concerns include uneven surfaces, limited manoeuvring space, insufficient crossing time, and a lack of visual and audible warnings. This research defined accessibility performance by the frequency and severity of pedestrian accessibility concerns. An environmental scan and series of site assessments at level railroad crossings in Winnipeg, Canada were performed to understand the capabilities of pedestrians, identify current engineering practices in Canada for accommodating pedestrians at level railroad crossings, and identify accessibility concerns. The knowledge obtained in this research was synthesized to develop a site assessment tool for evaluating pedestrian accessibility at level railroad crossings. The findings from this research expand the knowledge available on level railroad crossing accessibility and provide insight into specific considerations for pedestrian accommodation. The resulting site assessment tool can be used by practitioners to assess the accessibility performance of level railroad crossings and identify the presence of accessibility concerns in their jurisdictions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".