Developmental changes in how pedestrians cross streets in single- vs. dual-lane traffic conditions
Bibliographic record
Abstract
OBJECTIVE: Pedestrian injury is a leading contributor to childhood deaths. This study compared the crossing behaviors of children with adults when crossing in virtual single-lane vs. two-lane traffic conditions. METHOD: Using a fully immersive virtual reality system, children 7-10 years and adults crossed streets with traffic in one lane (near lane) or in two lanes (near and far lane) with 0 offset so cars from opposing directions arrived at the child's crossing line at the same time. On each trial, pedestrians made a decision on when to cross and then completed the crossing, with measures of attention and crossing behaviors automatically recorded by the system as they did so. RESULTS: In comparison to crossing a single lane of traffic, crossing two-lane roads increased injury risk for all pedestrians, though children experienced significantly greater risk than adults. Children predominantly crossed by stopping before entering the far lane, whereas adults showed greater synchronization of self-movement to traffic flow and more often crossed both lanes without stopping. CONCLUSIONS: Children experience more high-risk outcomes than adults when crossing single-lane roads. Crossing two-lane roads elevates risk for pedestrians of both ages, though this risk is significantly greater for child than adult pedestrians. The predominant strategy used to cross two lanes of traffic shows significant developmental changes. Implications for injury prevention are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".