Estimating Potential Effect of Speed Limits, Built Environment, and Other Factors on Severity of Pedestrian and Cyclist Injuries in Crashes
Bibliographic record
Abstract
Road facilities in urban areas are a major source of injury for nonmotorized road users despite the benefits of nonmotorized transportation. In particular, large Canadian cities such as Montreal face serious problems with pedestrian and cyclist safety. To address these problems, funds are continually allocated through different safety improvement programs such as reduction of speed limits, improvement of intersections, and increased traffic enforcement. However, few analytical tools help to identify and quantify the benefits of countermeasures (e.g., roadway design, speed management strategies, or land use policies) in reducing accident frequency and severity. Injury severity models were developed to determine the effects of road design, built environment, speed limits, and other factors (e.g., vehicle characteristics and movement type) on injury severity levels of pedestrians and cyclists involved in collisions with motor vehicles. Sources of data included police reports describing vehicle–pedestrian and vehicle–cyclist collisions, as well as information on land use, transit network, and road design attributes from the city of Montreal. The impacts of road design, land use, built environment, and other strategies on the injury severity levels of vulnerable road users were investigated. Factors such as darkness, vehicle movement, whether an accident occurred at an intersection, vehicle type, and land use mix affected the severity of pedestrian injuries from collisions. For cyclists, however, only vehicle movement and whether the accident occurred at a signalized intersection had significant effects on the severity of the injury.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".