The influence of crossing controls and crossing location on severity of injury among urban pedestrians
Bibliographic record
Abstract
Background Pedestrian injuries impose a significant burden on society, particularly in urban environments. The objective of this study was to determine the relationship between severity of pedestrian injury and presence of crossing controls at intersections and midblock locations in a large urban centre. Methods The data were obtained from the City of Toronto's Traffic Data Centre and Safety Bureau. All police-reported motor vehicle collisions involving pedestrians between 1 January 2000 and 31 December 2005 were included. Logistic regression was used to assess the relationship between injury severity and the location of the collision. Results There were 98.6/100 000 motor vehicle versus pedestrian collisions. 11/100 000 of these collisions resulted in severe injury and 1.4/100 000 resulted in fatal injury. At intersections, absence of a crossing control conferred a 1.8 greater odds of severe injury and a four times greater odds of fatal injury compared to where controls were present. At midblock locations, crossing controls did not reduce the odds of a severe or fatal injury; there was a 1.5 times greater odds of either severe/fatal injury at midblock locations compared to intersections with crossing controls. Discussion More intersection crossing controls would decrease the severity of pedestrian injury by slowing down traffic. Midblock crossing controls do not appear to have the same effect. There were 86 fatalities which occurred at uncontrolled midblock locations and overall, midblock fatalities represented 44% of all fatal pedestrian collisions. Effective strategies to deal with pedestrian midblock collisions are essential, considering the large injury burden these collisions represent.
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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 | 0.000 |
| 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".