Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Roundabouts
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Bibliographic record
Abstract
Connected and automated vehicles (CAVs) are expected to transform future transportation systems. Over time, these vehicles might enhance traffic efficiency and safety, especially at urban intersections. Therefore, it is essential to make adaptations to the traffic analysis models that are currently designed for human-driven vehicles only. This paper aims to assess the impact of CAVs on the entry capacity of roundabouts and develop an approach to adjust the capacity values calculated by the Highway Capacity Manual (HCM) for planning level analysis. Both single- and double-lane roundabouts are studied under various CAV market penetration rates and conflict flow rates in this paper. A specific CAV application, cooperative adaptive cruise control (CACC), is evaluated in this study because it enhances the car-following behavior at the roundabout entrance and has the best potential for improving the entry capacity. The simulation results indicate that the introduction of CAVs can substantially improve the entry capacity as the market penetration rate increases for both single- and double-lane roundabouts. The capacity improvement is more significant in the single-lane roundabout than in the double-lane roundabout. The capacities under different CAV market penetration rates and conflict flow rates are calculated and compared with the capacity results estimated from base models in the HCM to acquire the adjustment factors. Finally, a table of capacity adjustment factors is provided for the future implementation of HCM models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it