Evaluating Impacts of Public Transit and Automobiles During Connected and Automated Vehicle Adoption
Bibliographic record
Abstract
The adoption of connected and automated vehicles (CAVs) and automated vehicles (AVs) is expected to improve overall travel efficiency in traffic networks. However, this improvement may vary across multimodal road traffic, influencing the equity of travel experiences among different income groups. This study evaluates the equity effects of AV deployment during its gradual adoption in public transit and automobiles. Five scenarios with varying penetration rates are designed to represent the transition from HVs to CAVs in public transit and automobiles. Different model parameters were calibrated to represent HVs, AVs, and CAVs, and four measurements of effectiveness were developed to compare travel experiences between high‐income individuals, who primarily rely on private automobiles, and low‐income individuals, who depend on public transit. A detailed case study was conducted using real‐world road network and traffic flow data from Madison, Wisconsin. Simulation results reveal that while AV adoption improves overall traffic efficiency, including nonupgraded vehicles, it also exacerbates disparities in travel performance between high‐ and low‐income groups, even with efforts to expand AV‐enabled public transit.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".