Enhancing Safety Measures at Stop-Controlled Intersections: A Study on LED Backlit Signs and Drivers’ Behavior in Montréal, Québec
Bibliographic record
Abstract
This study evaluates the safety impacts of upgrading traditional STOP signs to light-emitting diode (LED)-illuminated backlit STOP signs at urban intersections, aiming to address visibility and conspicuity concerns that affect driver behavior and intersection safety. STOP signs are critical for regulating traffic flow and minimizing conflicts, yet their effectiveness can diminish under low-visibility conditions. To assess the effectiveness of LED-enhanced signage, a before–after study was conducted using surrogate safety measures. Key performance indicators included vehicle speeds, driver compliance rates, and vehicle-to-vehicle interactions, recorded both prior to and following LED implementation. A multinomial logistic regression model was used to analyze driver behaviors, and a calibrated microscopic simulation model, optimized using a genetic algorithm (GA), was applied to estimate traffic conflict frequencies. Video data were processed to extract driver trajectories and reactions under varying signage conditions. Results showed LED STOP signs improved compliance rates from 60% to 85%, reduced average vehicle speeds by 25%, and increased post-encroachment times. Conflict analysis revealed significant reductions in vehicle-to-vehicle and pedestrian conflicts, particularly at night. These findings highlight the effectiveness of LED signage in enhancing intersection safety and offer important implications for urban traffic management and the adoption of advanced traffic control technologies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".