Analysing and Mitigating Queues at Signalized Intersections Adjacent to Railway Crossings (Poster)
Bibliographic record
Abstract
Traffic signal control and railway crossing active warning systems provide the highest degree of control available at intersections and railway crossings, short of grade separation. Two issues may occur due to the close proximity of traffic signals and a railway crossing with an active warning system: Influence Zone (signal to tracks) queue; Gate Spill Back (tracks to signal) queue. Where a signalized intersection exists in close proximity to a railway crossing, signal pre-emption may be used, which requires coordination between traffic signals and the railway warning system. Signal pre-emption serves to ensure that the actions of these separate traffic control devices complement rather than conflict with each other. The Region of York identified six signalized intersections that appeared to be regularly extending from the traffic signals past a nearby set of railway tracks. According to the Transport Canada RTD-10 guidelines, signal pre-emption should be considered. The Region wished to examine the underlying causal factors that were contributing to the queues at these locations, in order to determine alternative solutions (other than signal pre-emption). The purpose of this project is to present a methodology for analyzing and characterizing queues at signalized intersections in addition to identifying techniques for evaluating the effectiveness of potential mitigating solutions. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".