Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Roundabouts
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".