Effect of human-driven, autonomous, and connected autonomous vehicles on geometric highway design
Bibliographic record
Abstract
Highway geometric design plays a crucial role in maintaining traffic safety and operational efficiency. The number of Autonomous Vehicles (AVs) and Connected Autonomous Vehicles (CAVs) on highway networks has increased in recent years. In this study, a traffic model is developed from a spring-mass system theory perspective to investigate traffic dynamics on horizontal highway curves. The Intelligent Driver (ID) model is based on a constant exponent δ to characterize driver response, which is unrealistic. By utilizing a spring-mass system analogy, the proposed model provides a more accurate and realistic representation of traffic. This model is used to evaluate the behavior of Human-driven Vehicles (HVs), AVs, and CAVs over a 1300 m circular road. The results obtained show that CAVs have better performance compared to HVs and AVs on horizontal curves, leading to better understanding of safety and efficiency on roads. Further, CAVs improve energy efficiency and emission reduction, contributing to effective and sustainable transportation systems. In addition, the results indicate that the proposed model has better performance compared to the ID model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".