Radar-Activated LED Stop Sign in a Rural Setting - Pilot Project in Saskatchewan
Bibliographic record
Abstract
Human factors indicate that too little stimulation can lead to driver inattention, which is a contributor to crash frequency. Saskatchewan is characterised by long, straight, rural roads with low driver workload. It is likely that low driver workload may be a factor in preventable collisions at the junction of Provincial Highway 35 and Provincial Highway 16 (Yellowhead Highway), which has experienced multiple fatal and injury collisions. The purpose of this paper is to discuss the implementation and current operation of the radar activated LED Stop signs at the junction of Provincial Highway 35 and Provincial Highway 16, which were installed by the Saskatchewan Ministry of Highways and Infrastructure (SMHI) in 2012 to mitigate a trend in right angle collisions. The existing traditional intersection improvements included illumination, Stop Ahead signs, dual oversize Stop signs equipped with red flashing beacons, and transverse pavement rumble strips. Ten years of collision data indicated that the trend in right angle collisions was not affected by the existing improvements, which were implemented over several years. The sight lines to this intersection are unobstructed and the Stop signs with red flashing beacons are visible from vehicles approaching several hundred metres away. When the Stop signs with the continuously flashing beacons were replaced with the LED Stop signs, the beacons were reinstalled on the intersection light standards so collision trends could be monitored without changing more than one aspect of the intersection at a time. The radar-activated lights on the Stop sign operate differently than continuous beacons in that they are only activated when vehicles approach the intersection at a speed that suggests they will not be able to comfortably decelerate to stop safely. Comfortable deceleration distances were calculated with sign mounted radar aimed at the distance where drivers will have to “slam on brakes” in order to stop at the junction. The pilot project duration is a minimum two years, after which SMHI hopes to determine whether the LED Stop signs should be considered as a safety improvement at other locations throughout the provincial network. The goals of the pilot project are to determine the reliability of the system components, how much maintenance it requires, the cost to maintain the system, and to determine the effects of the system on traffic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".