Impacts of Connected and Autonomous Vehicles with Level 2 Automation on Traffic Efficiency and Energy Consumption
Bibliographic record
Abstract
Evaluating the economic benefits of traffic optimization from connected and autonomous vehicles (CAVs) and relevant traffic organization methods is significant, which will help to put forward suggestions for policymakers to promote the application of CAVs. The impacts and related benefits from CAVs with level 2 automation (L2 CAVs) on traffic efficiency and energy consumption of expressways are analyzed in this paper. Average travel time and actual road capacity are on behalf of traffic efficiency while average electric energy consumption is used to compute traffic energy consumption. The corresponding traffic economic benefits consist of travel-time-saving benefits, road construction benefits, and energy-saving benefits. A benefit evaluation framework is newly proposed and microscopic traffic simulation software is applied as the experiment platform. Different market penetration rates of L2 CAVs and various traffic flow statuses are considered. Besides, dedicated lanes for CAVs are also involved in this research, which are regarded as a traffic organization method expected to promote the realization of CAV’s traffic benefits. It is found that L2 CAVs can save the average travel time and reduce average energy consumption for a single vehicle in most scenes. However, negative impacts on energy consumption are observed in several scenes due to the increase of actual road capacity. Positive economic benefits are obtained as soon as the traffic flow rate is out of saturation, which become increasingly higher as CAV’s market penetration rate turns larger. Additionally, amplification in traffic economic benefits appears only if CAV lanes are provided under proper conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".