Exploration of the impact of junction characteristics on pedestrian red-light violation
Bibliographic record
Abstract
The design of the geometry and traffic controls at signalised junctions is often focused on the level of service offered to motor vehicles and rarely considers the level of service offered to pedestrians. This results in pedestrians adopting illegal and unsafe behaviours – for example, red-light violation. This study aimed to identify the elements of signalised junction design that are critical in pedestrian safety by analysing how they affect pedestrian behaviour. Both traffic engineering design and associated traffic conditions were investigated. Over 6500 observations were made at ten signalised junctions in Montreal, Canada. The ten junctions were selected to cover a variety of environments, road users and junction designs. The results show that the presence of a countdown display has the most significant and positive impact on pedestrian behaviour. The results also suggest that pedestrians cross on the red light when they feel confident about their ability to judge whether they can use the available traffic gaps to cross the street safely. This study concludes that an adequate junction design is likely to limit risky pedestrian behaviours. Therefore, designers need to consider the factors affecting the behaviour of pedestrians to design junctions that are convenient and safe for them.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".