Crash Severity Analysis at Roundabouts: A case study in Quebec, Canada
Bibliographic record
Abstract
Numerous studies both in North America and abroad have studied the roundabout as a means to improve the capacity and safety of intersections. The focus of these studies has been to reduce both the number and the severity of accidents, and have generally come to agreeable results. Nevertheless, few studies have dealt with the identification of accident factors within roundabouts. The varied physical and environmental characteristics of Quebec’s roundabouts and the behavioral traits of its drivers requires further study in order to help reduce the occurrence and severity of accidents. This paper presents a crash severity analysis to identify the effects of different contributing factors on injury severity outcomes. To achieve this, an ordered logit modeling technique was used for the analysis. Moreover, an important effort was made to integrate an accident database containing accident records from the year 2000 to 2011. Among other results, it was found that factors such as a larger number of involved vehicles, accidents occurring within the intersection, vehicle rollovers, the involvement of buses, accidents occurring in the dark on unlit roads and snow conditions led to increased injury
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".