Modelling Fatal Pedestrian Accidents In Montreal's Metropolitan Area 1995-1997
Bibliographic record
Abstract
In order to help prevent pedestrian accidents, it is necessaq to identify the environment and circumstances of the accidents and the characteristics of the persons involved. To study these issues a three component model has been elaborated, the first component included characteristic of the environment at the locus of the accident, the second component characteristics related to the driver involved in the accident and the type of vehicle and the third component characteristics relating to the fatally-injured pedestrian. Using a logistic regression as a method of analysis we decompose the variation in fatal pedestrian automobile accidents between the city of Montreal and the periphery of the region of Montreal. Results of the study showed that age of the pedestrian killed in traffic collisions is an important explanatory variable for the tsvo territories. The elderly are more likely to be involved in fatal pedestrian crashes than are the other age groups. Some variables related to the characteristics of the driver and those of the striking vehicle were included in the final model but there are some differences between the two territories for example, high posted speed limits is associated with fatal pedestrian crashed within the city of Montreal but was not statistically significant at the periphery. Variables related to the characteristics of the environment at the site of the accident were roadway alignment and lighting conditions. Roadway alignment (grade-curve and flatstraight categories) is an important explorato~ variable for the periphery and lighting conditions - roadway lighted at night for the city of Montreal. In conclusion, from the demographic and environmental point of view, it is important to make a distinction between geographical areas for exploring the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".