Vehicle Size and Risk of Side-Impact Collisions: A Case Control Study in Toronto and Montreal
Bibliographic record
Abstract
Vehicle size has long been a concern in traffic crashes when examining the risks of injury to vehicle occupants. It has less frequently been examined as a risk factor for crashing in the first place. This report describes a case-control study of 61 side-impact crashes in Toronto and Montreal to see which vehicle characteristics are associated with crash risk. The crashes were part of the on-going Transport Canada project of detailed crash investigations of side-impact crashes. For each vehicle, observers returned to the site of the crash and identified four control vehicles traveling in the same direction as each of the two crashing vehicles. From the license numbers of these vehicles, the authors obtained the Vehicle Information Number (VIN) and thereby details of engine size, wheelbase, curb weight and other specifications of each vehicle. The paper used conditional logistic regression to compare crashing vehicles and their matched controls and estimate the odds of crashing as a function of vehicle characteristics; when required, separate odds were estimated for struck and striking vehicles. Several vehicle characteristics were associated with crash risk; however, many were significantly different for struck and striking vehicles. For example, engine size had increased odds of crashing as a striking vehicle (OR = 1.74 per 1000 cc.) but decreased odds of crashing as a target vehicle (OR = 0.72 per 1000 cc.). Safety factors, such as anti-lock brakes and traction control showed a substantial protective effect for both target and bullet vehicles. The explanation for these findings may relate to conspicuity; it may also relate to the ways vehicle size and power affect driver behavior at intersections and in other situations where traffic conflicts occur. The case-control method described in this study is relatively simple as well as economical. It is a useful method to evaluate road safety and vehicle characteristics without the need for large detailed databases maintained in many developed countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".