Investigating factors that affect conflicts between bicyclists and right turning vehicles at signalized intersections
Bibliographic record
Abstract
Due to the recognition of active transportation as a beneficial alternative to more traditional modes of transportation, there is increasing interest in designing infrastructure systems to promote the safe use of forms such as bicycling. Collision data, which are typically used in road safety studies, may be incomplete and the use of such data is reactive as it requires collisions to have already taken place. Additionally, collisions involving bicyclists and vehicles are less common than those between vehicles. As a result, alternative approaches for evaluating the safety effect of various infrastructure attributes, such as the use of surrogate safety measures, specifically traffic conflicts, could be considered. Most collisions between bicyclists and vehicles occur at intersections and of these, collisions between right turning vehicles and bicyclists form the majority. The main objective of this study was to use cross-sectional regression models to investigate various intersection characteristics, including geometry, signal phasing, and bicycle infrastructure, with a view to determining which attributes are associated with a significant effect on right turning conflicts and how this may vary with different conflict severity levels. Using data for 65 signalized intersection approaches across Canada, various intersection attributes were found to be associated with the frequency of right turning conflicts such as exposure levels, the lateral offset of the bicycle facility and the grade of the approach to the intersection. The significance levels of the associated effects of certain attributes were observed to change with the severity level of the conflicts.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".