Macroscopic Experiments on Coexistence of Autonomous Vehicle Behavior on Various Heterogeneous Traffic Conditions
Bibliographic record
Abstract
Automated vehicles (AVs) are likely to bring paradigm shift in the future of transportation and in the transformation of urban space as they could reduce traffic accidents, energy consumption, and pollution while also lowering congestion expenses. To provide meaningful insights, there is a substantial need for investigation into the macroscopic evaluation of various evolutions of AVs using several measures of effectiveness. The main focus of this study is to evaluate the macroscopic operational impacts of AVs in terms of their driving logics, automation levels, and roadway type, all of which are adopted based on their passenger car unit (PCU) factors at various penetration rates, in order to assess the coexistence of AVs with heterogeneous traffic. The daily vehicle hours travelled, daily vehicle kilometers travelled, sum of delays on links, speed variation, and sum of vehicle volumes on links are used as measures of effectiveness parameters based on outputs of PTV Visum scenario manager. The results of the various scenario combinations depicted an overall improvement with advancement of driving logics, automation levels, and roadway types for each studied parameter. For instance, for better roadway condition with the motorway scenario and at higher AV penetration, the cautious driving behavior negatively affects the network performance, whereas favorable improvements are observed for the parameters of the normal and aggressive driving behaviors. Decision makers could make use of the insights obtained from the results to further shape the AV deployment aspects and extend the study considering infrastructure AV-readiness along with AV communication systems.
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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".