Comparing Safety at Signalized Intersections and Roundabouts Using Simulated Rear-End Conflicts
Bibliographic record
Abstract
The safety implications of adopting roundabouts in place of conventional signalized intersections have not been adequately assessed. A microscopic simulation model was used to compare the pattern of traffic conflicts at roundabouts with conflicts for signalized intersections. Three indicators of safety performance were defined: (a) time to collision (TTC), (b) deceleration rate to avoid the crash (DRAC), and (c) crash potential index (CPI). For each indicator, traffic conflict profiles were obtained in terms of number of vehicles in conflict and number of conflicts per vehicle for selected directional maneuvers. The exposure time to conflict for each maneuver and vehicle was also determined. Twelve combinations of geometric and traffic attributes (traffic scenarios) were simulated over a 15-min period. The results suggested that roundabouts yield reduced exposure times to rear-end conflicts compared with signalized intersections. On average, signalized intersections also reflected increased number of vehicles in conflict and percentage of vehicles in conflict compared with roundabouts. This relationship was found to be independent of input volumes and pavement surface condition and applied consistently to all safety indicator measures (TTC, DRAC, and CPI).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".