Speed Reduction Profiles Affecting Vehicle Interactions at Level Crossings with No Trains
Bibliographic record
Abstract
Observed road vehicle speed and deceleration profiles at active level crossings in the absence of a train are investigated and linked to safety performance. Results indicated that speed and deceleration profiles could be segmented into two zones along the approach road. Speed reduction was initiated at an upstream point along the road about 60 m from the track (Zone 1), and this became more pronounced for the road segment 20 to 30 m before the track (Zone 2). Although the deceleration rates based on average vehicle speeds were found to be within comfortable thresholds in both approach zones, a safety concern was raised for the worst-case scenario in which lead vehicles were reducing their speeds at a higher rate than following vehicles. Safety performance using an average crash potential index per vehicle was found to increase in the vicinity of the track for a given case study crossing application. The range of safety performance values was also found to be greater in Zone 2; this finding suggested possible safety concerns about rear-end crashes taking place near the track and the added possibility of a vehicle being pushed onto the track in the path of an oncoming train. This study provides useful insight for modifying speed profiles in a traffic simulation model to account for the presence of a level crossing. The analysis also provides insights into a base crash risk for an open crossing. These insights can be used to refine previous estimates of countermeasure effectiveness, including closures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".