Evaluation of Macroscopic Fundamental Diagram Transition in the Era of Connected and Autonomous Vehicles
Bibliographic record
Abstract
The introduction of connected and autonomous vehicles (CAVs) could bring practical solutions to the existing challenges with transportation infrastructures such as accidents and congestion. However, the transition to the era of CAVs would be gradual, and it could be expected that both CA V sand human-driven vehicles (HDVs) would exist in the network for some time, which could change the fundamental properties of urban networks. In this paper, the impact of CAVs on macroscopic fundamental diagram (MFD) is analyzed with microscopic traffic simulations, and the sensitivity analysis of market penetration rates of CA V s and network configurations is conducted. The analysis shows that one-way grid networks offer the most accessible and resilient environment during various phases of CA V introduction. Moreover, the introduction of CA V s not only improves the aggregated network performance but also improves the accessibility (trip completion rate) of regular HDVs.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".