Active Warning System for Highway-Rail Grade Crossings Using Connected Vehicle Technologies
Bibliographic record
Abstract
Highway-rail grade crossing (HRGC) collisions are a significant safety concern around the world. HRGC collisions have a high risk of injuries and fatalities. To mitigate that risk, safety countermeasures for both active and passive HRGCs have been implemented. Leveraging the latest developments in connected vehicle (CV) technologies, CV-based warning systems perform well in safety applications for roadway networks. However, few have been developed to focus on safety improvements specifically for HRGCs. To bridge this gap, this paper proposes a novel active warning system that was created with readily available CV technologies and devices. A crossing risk assessment model was developed and evaluated in simulation and field applications. The proposed model predicts the crossing risk probabilities in the near future. When road users are in great risk of a collision, the warning system sends out auditory and visual alerts and displays the estimated waiting time. The test results reveal that the proposed warning system is promising for field implementation to improve safety at grade crossings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".