Using Microsimulation to Evaluate Safety and Operational Implications of Newer Roundabout Layouts for European Road Networks
Bibliographic record
Abstract
“Standard” roundabouts, for example those designed in some European countries, can often be characterized by low levels of safety or capacity and a high degree of sustainability. Given the proliferation of newer layouts, it is of interest to explore whether design practices could be improved by capitalizing on the experience gained internationally. Operational aspects of some of these designs have been explored previously, but there is a need to compare both the operational and safety performance of new designs to that of standard roundabouts. The objective of this paper is to evaluate the safety and operational implications of various potential alternatives to the standard roundabouts that proliferate in Europe and elsewhere. Microsimulation is used to simulate traffic operations at roundabout layout alternatives at the same levels of volume to capacity (V/C) ratio and also with the same traffic flow. Operational performance measures include the common level of service parameters, while measures of safety are based initially on time to collision (TTC) values. Threshold values of TTC were then applied in defining conflicts that are used for crash-based safety evaluation by applying crash-conflict models estimated in published research. Interesting insights were revealed, suggesting that the newer layouts should be considered where warranted by cost-benefit considerations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".