Quantifying mobility impacts of railway grade crossing blockages on vehicular traffic and emergency responders
Bibliographic record
Abstract
With increases in road and rail traffic, there is heightened public concern about the mobility and safety impacts of highway-rail grade (level) crossings. Public transportation agencies need analytical tools to evaluate the efficacy of options available to address these concerns and criteria-based prioritization approaches that support crossing upgrade decisions. This research presents a series of projects that contribute knowledge by: (1) identifying criteria used when implementing grade separations at grade crossings; (2) quantifying impacts of blocked grade crossings on road traffic operations; and (3) quantifying the risks of blocked grade crossings for the emergency response system. The first project synthesizes findings from a review of literature and practice on mobility impacts when a train occupies a crossing. This project demonstrates the importance of mobility impacts at blocked crossings, identifies and compares mobility-related decision criteria and actionable thresholds used within prioritization approaches to rank crossings for grade separation, and reveals methods to quantify and monetize delay at blocked crossings. The second project develops and applies a methodology that incorporates crossing blockage data, conventional traffic data, and vehicle probe data into a network-level traffic microsimulation model designed to quantify the mobility impacts of crossing blockages. The model estimates these impacts under various recovery signal timing plans to measure intersection performance and manage queues. The results demonstrate that adjustments to signal timing plans can improve queue clearance following a crossing blockage. More generally, the microsimulation model can be tailored to evaluate the mobility impacts of operational treatments at grade crossings, which tend to be less costly than grade separation. The third project develops a probabilistic methodology to quantify the risk of crossings blockages to emergency response (ER) vehicles and ER stations. Through two case examples in Winnipeg, Canada, the analysis finds that 13.2% of the ER vehicles that traversed the studied crossing experienced a crossing blockage delay. Likewise, 0.5% of the ER trips dispatched from the studied station experienced a crossing blockage delay. Overall, this research contributes new knowledge about mobility impacts caused by crossing blockages and reveals new opportunities to mitigate those impacts to the benefit of communities and society at-large.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".