Comparing Relative Safety of Railway Transport Level Crossings by Data Envelopment Analysis
Bibliographic record
Abstract
Data envelopment analysis (DEA) has been widely used for analyzing the safety of different modes of transportation. Its recent applications for level crossings have been successful to compare countries according to the safety of their level crossings or comparing relative safety of level crossings. This paper provides the most comprehensive DEA model which can assess the relative safety of level crossings by considering daily train traffic, daily vehicle traffic, number of railway tracks, number of road lanes, maximum speed of trains and road vehicles, the number of accidents, injuries and fatalities in the past. The model is used for the case study of all level crossings in Canada and is followed up by two Tobit regression models to investigate the impact of region and protection type. The proposed method can help railway policymakers and practitioners worldwide in ranking the safety of level crossings, prioritizing improvements, comparing different regions and achieving an overall decrease of accidents and fatalities at critical points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".