Assessing the Impact of Large-Scale Trends on Ontario’s Pedestrian Fatality Rate
Bibliographic record
Abstract
Pedestrian-involved collisions are a key contributor to roadway fatalities in Ontario; pedestrian deaths have been growing as a proportion of total road fatalities. This study aimed first to determine trends in the pedestrian fatality rate in Ontario over time and, second, to assess the impact of select large-scale trends on pedestrian fatalities. Large-scale trends were identified through a review of the literature and hypotheses were tested using Ontario collision data from 2002 to 2016. The following four key areas were assessed for their impact: (1) the aging demographic; (2) the impact of increasing consumer preference for light trucks; (3) the potential for an increase in alcohol-consuming pedestrians associated with a decrease in alcohol-consuming drivers, and; (4) increasing inattention, caused, in part, by pedestrians and drivers using electronic devices. A quadratic model, with a minimum at 2010, best described changes in Ontario’s pedestrian fatality rate, suggesting a transition from a decreasing to increasing trend at that time. Results of the four key areas were: (1) the proportion of pedestrians aged 75 and older being killed has been increasing over time, a trend that can be fully explained by their increased representation in Ontario’s population, a trend which is expected to continue; (2) similarly, the increase in the proportion of pedestrians killed by a light truck can be explained by their increased representation in Ontario’s registered vehicle population; (3) the odds of a pedestrian being alcohol positive have been decreasing over time; and (4) the odds are higher that a driver who kills a pedestrian is inattentive.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".