Policy Efforts to Promote the Adoption of Autonomous Vehicles: Subsidy and AV Lanes
Bibliographic record
Abstract
Autonomous vehicles (AVs) have the potential to improve safety, traffic capacity, and energy efficiency, but these advantages can only be realized when the AV market penetration is sufficiently high. To promote the adoption of AVs, it would be crucial for the government to take policy measures. This paper develops a two-stage model to explore the effects of subsidy and AV lanes’ policies on AV adoption. In the first stage, given the subsidy policy, the vehicle manufacturer sets AV price to pursue maximum profit while anticipating the choice of consumers for AVs or conventional vehicles (CVs), from which the AV market penetration can be accessed. Subsequently, based on the AV market penetration acquired from the first stage model, an optimization model integrating the mixed traffic assignment is developed in the second stage to determine the time-dependent progressive AV lanes’ deployment plan. The first and second stage models are solved using the simulation-optimization and genetic-algorithm-based approaches, respectively. Due to the mutual influence of the two models, an iterative optimization approach is applied to solve the whole model. Two numerical experiments are conducted, and the results demonstrate the positive effects of subsidy and AV lane policies on increasing AV market penetration. The analysis provides significant managerial insights for policymakers to promote the development of AVs.
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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 | 0.001 |
| 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".