<i>Highway Capacity Manual</i> Capacity Adjustment Factor Development for Connected and Automated Traffic at Signalized Intersections
Bibliographic record
Abstract
Connected and automated vehicles (CAVs) will potentially improve transportation system performance. Guidance on the capacity impact of CAVs at different market penetration rates (MPRs) will help agencies to incorporate the effects of CAVs when planning and designing roadways. Traditionally, practitioners have used the Highway Capacity Manual (HCM) to assess capacity and evaluate the quality of service for various facilities and systems. Although several studies have provided insight into the capacity benefits of CAVs, there is a need for quantified CAV effects that can be used to develop HCM guidance. In this study, the capacity benefits of CAVs at signalized intersections were estimated, and capacity adjustment factors (CAFs) were developed for the HCM. The researchers considered variations in CAV gap/headway settings, platoon lengths, turning movement types (through and left), and left-turn phasing modes (protected versus permitted). Microscopic traffic simulation was used to model CAVs. The results showed that performance indicators such as saturation headway gradually improved with increases in CAV MPR, resulting in up to 40% capacity increase at 100% MPR for the protected movements. For permitted left turns, up to 45% capacity increase could be achieved at 100% MPR. This increase in permitted left-turn capacity can be attributed to vehicle-to-vehicle (V2V) communication, which provides advanced information on available gaps in conflicting traffic and reduced follow-up headway time both for permitted left turns and the opposing through movement. Based on the capacity results, this study provides CAF tables for CAVs that can be easily integrated into the HCM and used for planning-level guidance by practitioners.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".