A Risk-Based Decision Support Framework for Railway-Highway Grade Crossing Closures
Bibliographic record
Abstract
Reducing the risk of collisions between trains and vehicles at railway-highway grade crossings is a high priority safety strategy set by many governments and railway authorities. To achieve this goal, one of the main engineering approaches used is to permanently close some grade crossings. Although this approach can completely eliminate the collision risk at the grade crossings being closed, it could have a huge impact on the road traffic, resulting in a significant increase in travel time for road users. This can also lead to some secondary problems, such as increased trespassing risk. Thus, the problem of which crossings should be closed must be addressed with a careful consideration of all benefits and costs that could result from the closure. This research aims to develop a specific framework for determining the priority of grade crossing closure and develop models that can be used to quantify the safety benefit and the costs. \n \nIn this study, a risk-based framework is proposed, including a preliminary screening and a cost-benefit analysis module. In the preliminary screening step, all the crossings in the area of interest are first examined on the basis of a set of pre-established rules or criteria to remove those crossings that should definitely not be considered for closure due to their critical importance to the road traffic. This step yields a set of candidate crossings. All individual crossings in the candidate set are then involved in the cost-benefit analysis module. This module determines the expected safety benefit, travel time cost, and construction cost that could result from their closure. The safety benefit of closing a given crossing is estimated using a set of collision risk models for collision frequency and collision severity. These models are calibrated using the latest crossing inventory data and six-year long collision history data (2013-2018). To estimate the extra travel time cost that road users would experience due to the closure of a crossing, an accessibility analysis tool is created in a ArcMap to calculate the extra travel distance, using the spatial data of road and railway network. Lastly, the life-cycle benefit-cost ratios of all candidate crossings for closure can be calculated and used as a ranking criterion for determining their priority of closure. The application and rationality of the proposed framework are examined through a case study of three provinces in Canada.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".