Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections
Bibliographic record
Abstract
This paper proposes a new approach to represent cyclist risk exposure. This approach considers disaggregate motor vehicle and cyclist flows and develops cyclist injury frequency models. Three definitions of risk exposure were used in this research, including aggregated flows, motor vehicle flows aggregated by movement type, and potential conflicts between motor vehicles and cyclists. As an application environment, a large sample of signalized intersections on the island of Montreal, Quebec, Canada, was used, along with data that comprised disaggregate motor vehicle and cyclist flows. Several negative binomial models were fitted to the data. This study showed that cyclist collisions were sensitive to changes in both cyclist and motor vehicle flows. A 10% increase in bicycle flow was associated with a 4.4% increase in the frequency of cyclist injuries. A 10% increase in the total number of motor vehicles that passed through the intersection would result in a 3.4% increase in cyclist injury occurrence. When motor vehicle flows were considered on the basis of movement type, right-turn movements had a great effect on injury occurrence. Similar results, which identified right turns as having the greatest effect on cyclist injuries, were produced when the impact of potential conflicts was determined. The number of bus stops in the proximity of the intersection increased cyclist injury occurrence. Some geometric design factors, such as the presence of a median, parking entrance, and the number of intersection legs, were tested. The effect, however, was found to be statistically nonsignificant.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".