An inclusive framework for automatic safety evaluation of roundabouts
Bibliographic record
Abstract
This article presents an approach for studying different aspects of traffic safety at roundabouts. An automated safety analysis framework is used to detect different types of traffic conflicts, as well as the inappropriate negotiations and the gap acceptance behavior of drivers. To test the validity of the proposed method, a case study is used for a roundabout in Doha, Qatar. Seven types of traffic conflicts are studied and their severity is identified using the time-to-collision conflict indicator. Four common types of driver inappropriate negotiations behavior are also investigated. The analysis shows that most of the inappropriate negotiations and traffic conflicts are due to drivers' poor lane discipline that can be partially attributed to the poor lane marking. Gap acceptance behavior is also studied by identifying lead, lag, and total gaps. The traffic conflicts, inappropriate negotiations, and gap acceptance results are validated by a comparison with manual observations. The results of the validation process show the viability of the automated approach that produces acceptable results with less time and effort. Moreover, the data collected using this approach provides further insights on roundabout safety evaluation and has the potential for use in the assessment of roundabout design.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".