Leveraging Dynamic Right-of-Way Allocation and Tolling Policy for CAV Dedicated Lane Management to Promote CAV and Improve Mobility
Bibliographic record
Abstract
With the capability of communicating with surrounding vehicles and infrastructures, connected and automated vehicles (CAVs) can safely drive closer with reduced headway, thereby potentially improving traffic efficiency. However, their superiority is compromised in the mixed traffic environment because of the interruption of human-driving vehicles (HDVs). In this circumstance, researchers proposed to physically separate CAVs and HDVs by deploying CAV-dedicated lanes (CAV-DLs). Nevertheless, the CAV-DLs may be underutilized, especially in low CAV penetration rate (PR) cases which may even reduce traffic efficiency. To solve this problem, two novel strategies were proposed in our study to better manage the CAV-DLs and magnify the benefit of CAVs: The first one is to dynamically allocate the right-of-way for CAV-DLs based on the predicted CAV-DLs’ effective utilization rate so that the HDVs can be allowed to use the dedicated lanes when they are not adequately occupied. The second strategy is motivated by the economic instrument, which allows HDVs to use the CAV-DLs by paying a toll. The toll is determined by the travel time difference between CAV-DL and general lane (GL), and these tolls can be utilized as subsidies to stimulate drivers to purchase CAVs for promoting their adoption. The two strategies were evaluated using the case study designed based on the network of Edmonton downtown area in Canada, and the results demonstrated that both methods can significantly reduce travel time. Besides, the two strategies were compared comprehensively in terms of their effectiveness and policy enforceability, which can provide some guidance for both traffic policymakers and practitioners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".