Development of conflict-based safety performance function for safety evaluation of urban roundabouts in India
Bibliographic record
Abstract
This study evaluates the safety performance of urban roundabouts in India using conflict-based safety performance functions (SPFs). High-resolution aerial videos were utilized to extract vehicle trajectories, and the surrogate safety assessment model was employed for conflict identification. Data were collected from 27 roundabouts across various Indian cities, focusing on non-lane-based heterogeneous traffic conditions. Four SPFs were developed utilizing a generalized linear model with negative binomial distribution and log link function for different proximity levels based on time-to-collision and post-encroachment time values. Key factors influencing conflict frequency included conflicting traffic volume, occupancy time, ratio of approaching and conflicting traffic volume, percentage of cars and motorcycles, inscribed circle diameter, roundabout symmetry, and number of legs. The developed conflict frequency prediction models fitted well and demonstrated robust predictive performance. This proactive framework for roundabout safety assessment can be followed to enhance road safety in developing nations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".