Spatial Variation of Injury Risk in a Metropolitan Area, According to Home Location, Transportation Mode, Distance Travelled and Route
Bibliographic record
Abstract
At the route level, traffic volume and road geometry greatly influence the risk of injury. At the state or country level, population rates of road injuries vary according to household density and transportation mode. This study aims to integrate these two perspectives to describe the spatial variation of injury risk according to home location within a metropolitan area. Police reports (2006-2010) provided injury data (e.g. crash location). Population trips, travel mode and routes were estimated using a representative Origin-Destination Survey (2008) for the entire Montreal Metropolitan area. Home location for each individual was classified into population density quintiles, using property assessment rolls. Regression models were developed to estimate the number of car occupant, bus occupant and pedestrian injuries, accounting for exposure to traffic as well as road geometry. These models were applied to predict the risk associated with each road, intersection and highway and then accumulated throughout each trip. Results show that in the densest sector, injuries per kilometre of road are six times greater compared to the least dense one. At intersections, road segments and on highways, the number of injuries is strongly associated with traffic volume and proxies of vehicle speed. Unsurprisingly, trips by public transit are safer than by car. At the individual level, the likelihood of injury increases with distance traveled. People living in the least dense sector make more trips by car and travel greater distances and are almost three times more likely to be injured than those in the densest sector. Within a metropolitan area, more injuries occur in the densest sector, however it is the population living in the least dense sector that have the greatest risk of injury. In urban settings, road injury prevention strategies should not only target road geometry but also urban development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".