The Impact of Traffic Lights on Dangerous Pedestrian Crossings and Violations: a Case Study in Montreal
Bibliographic record
Abstract
Pedestrian violations of signals and dangerous crossing situations at intersections are commonly observed. The objective of this study was to determine the impact of pedestrian waiting time at an intersection, due to phasing, time of arrival and the presence of a pedestrian signal on the proportion and type of pedestrian violations and dangerous crossing situations. Seven intersections with similar geometry and traffic conditions but different maximum waiting times, four of which had a pedestrian signal, were observed over at least two hours to collect crossing information. Data was collected manually for the main analysis and with a video camera for the validation of results. Many factors were identified as having an impact on the proportion of violations. Some confirmed the literature, such as age, sex, group size, conflicting vehicle flow and pedestrian signals. New factors were identified in this research such as maximum waiting time (red phase). It was also determined that an intersection’s clearing time had an impact on violations and on the proportion of dangerous crossings committed. Also, pedestrians’ speeds depended on the type of crossing. The results underline the importance of providing pedestrian signals and paying particular attention to pedestrian maximum waiting time as well as clearing time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".