A Platoon Formation Strategy for Heterogeneous Vehicle Types at a Signalized Intersection
Bibliographic record
Abstract
Many studies and real-world experiments have demonstrated the potential ability of connected and autonomous vehicles (CAVs) platooning to improve fuel efficiency and network performance. However, most studies ignore the impacts of vehicle types and their different acceleration rates in the platoon formation at the signalized intersection. To tackle this issue, this paper proposes a platoon formation strategy with heterogeneous acceleration rates of CAVs. The key idea is that the heavy truck starts earlier than the green starts but from a faraway stop line (that is customized by the intersection controller). An analytical model is developed to calculate the early starting time and prior starting point based on the vehicle's acceleration rate and position of vehicles in an approaching queue. Microscopic simulation results show that the proposed platoon formation strategy is able to reduce the start-up delay of the platoon, by as much as 50% compared to a traditional platoon. Sensitivity analysis suggests that density, market penetration rates have a significant impact on the performance of platoon formation.
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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) | 0.001 | 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".