Pedestrian crossing location influences injury severity in urban areas
Bibliographic record
Abstract
BACKGROUND: Pedestrian incidents represent an increasing proportion of serious injuries resulting from motor vehicle collisions in Canada. However, few studies have examined the effect of pedestrian crossing location in urban areas on injury severity. The objective of this study was to investigate the relationship between pedestrian-motor vehicle collision injury severity and crossing location. METHODS: This study was a population-based analysis of police-reported pedestrian collision data. The study group was pedestrian collisions from 1 January 2000 to 31 December 2009 in Toronto. Main outcome measures were a binary indicator of severe injury, and a four-level categorical variable of injury severity. The exposure variable was crossing at mid-block with no traffic control compared to signalised intersection. Analysis was via binary and multinomial logistic regression models to estimate ORs of injury severity with 95% CIs. RESULTS: The analysis included 9575 pedestrian-motor vehicle collisions, of which 7325 occurred at signalised intersections when crossing and 2230 occurred at uncontrolled mid-block locations when crossing without right of way. Uncontrolled mid-block collisions resulted in greater injury severity when controlling for road type. The odds of severe injury were 1.75 (95% CI 1.07 to 2.86) for children, 2.55 (95% CI 2.13 to 3.05) for adults and 1.68 (95% CI 1.23 to 2.28) for older adults. The odds of death at uncontrolled mid-block crossings were 4.97 (95% CI 3.11 to 7.94) in adults and 3.49 (95% CI 2.07 to 5.89) in older adults. CONCLUSIONS: Crossing at uncontrolled mid-block locations resulted in greater injury severity compared with crossing at signalised intersections. This has important implications for pedestrian behaviour and traffic environment design and emphasises the need for safe pedestrian crossings on urban roads.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".