Distractions or long waits? Impacts on risky crossing behaviour
Bibliographic record
Abstract
Pedestrian-vehicle conflicts at intersections are considered as a major source of injuries and fatalities. Intersections are a critical part of roadway design since pedestrians are exposed to different and potentially dangerous activities due to how an intersection is designed, but also what the pedestrian is doing and where in the city they are. In this study, various influences on risky crossing behaviour are examined. At the individual level, the influence of distractions and where people are looking before crossing are tested. Further, various intersection design variables including wait time, intersection size and speed limits, and contextual variables such as the built environment nearby and traffic flow are examined. The data was gathered by observing pedestrians at 24 intersections in Montreal and Quebec City (12 each). Logistic regression models were estimated to determine the influencing variables on four dangerous behaviours: a) start on red, b) finish on red, c) finish on red having started on green, and d) cross completely on red. Results demonstrate the importance of wait time on risky crossing behaviour with short wait times (< 30s) decreasing the likelihood of such behaviours considerably. For individual behaviour, having a cellphone in one's hand reduces the likelihood of starting to cross on red. In contrast, looking at traffic was over four times more associated with crossing illegally.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".